CMIP6 precipitation and temperature projections for Chile

Abstract

Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile’s climate. The analysis is focused on four distinct climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions, first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature observation-based gridded datasets over the historical period (1986–2014) and then we analyze the models’ projections for the end of the century (2080–2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability over different subregions. However, none of the models is best across all subregions for both precipitation and temperature. Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called “hot models” likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century reductions in mean annual precipitation for Central Chile (~ − 20 to ~ − 40%) and Northern Patagonia (~ − 10 to ~ − 30%) under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10–20%. Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred along the Andes Mountains. Northern Chile displays the strongest increment of up to ~ 6 °C in SSP585, followed by Central Chile (up to ~ 5 °C). Both Northern and Southern Patagonia show a corresponding increment by up to ~ 4 °C. We also briefly discuss about the environmental and socio-economic implications of these future changes for Chile.

Full text

Vol.:(0123456789)
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Climate Dynamics (2024) 62:2475–2498
https://doi.org/10.1007/s00382-023-07034-9
ORIGINAL ARTICLE
CMIP6 precipitation andtemperature projections forChile
ÁlvaroSalazar1,2 · MarcusThatcher3· KaterinaGoubanova4· PatricioBernal5· JulioGutiérrez1,2,4·
FranciscoSqueo1,2,4
Received: 1 June 2023 / Accepted: 18 November 2023 / Published online: 22 December 2023
© The Author(s) 2023
Abstract
Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled
Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile’s climate. The analysis is focused on four distinct
climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions,
first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature
observation-based gridded datasets over the historical period (1986–2014) and then we analyze the models’ projections for
the end of the century (2080–2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models
are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability
over different subregions. However, none of the models is best across all subregions for both precipitation and temperature.
Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called “hot models”
likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future
climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century
reductions in mean annual precipitation for Central Chile (~ − 20 to ~ − 40%) and Northern Patagonia (~ − 10 to ~ − 30%)
under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10–20%.
Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface
temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred
along the Andes Mountains. Northern Chile displays the strongest increment of up to ~ 6°C in SSP585, followed by Central
Chile (up to ~ 5°C). Both Northern and Southern Patagonia show a corresponding increment by up to ~ 4°C. We also briefly
discuss about the environmental and socio-economic implications of these future changes for Chile.
Keywords General circulation models· IPCC· South America· Andes· Climate projections· Chile
1 Introduction
With the advent of the Coupled Model Inter-comparison
Project phase 6 (CMIP6) multimodel ensemble (Eyring etal.
2016), new opportunities arise to investigate the climate
system at global and regional scales under a series of future
emission scenarios. CMIP6 builds upon previous CMIP5,
which are fundamental inputs to the Intergovernmental
Panel on Climate Change (IPCC) Assessment Reports,
AR6 and AR5 (IPCC 2013, 2021), respectively. It presents
a new framework of socioeconomic scenarios, named Shared
Socioeconomic Pathways (SSP), that are combined with the
Representative Concentration Pathways (RCP) of CMIP5
(Eyring etal. 2016; Meinshausen etal. 2020). This new
generation of climate models is of great value for evaluat-
ing future climate evolution in Chile, which appears as one
of the world’s regions most sensitive to changes in climate
(Ukkola etal. 2020).
An increasing number of studies are identifying some
improvements of CMIP6 compared to previous CMIP
ensembles. The new generation of models can better
* Álvaro Salazar
alvaro.salazar.p@gmail.com
1 Institute ofEcology andBiodiversity (IEB), Victoria 631,
Barrio Universitario, Concepción, Chile
2 Departamento de Biología, Facultad de Ciencias,
Universidad de La Serena, Casilla 554, LaSerena, Chile
3 CSIRO Environment, Aspendale, VIC3195, Australia
4 Centro de Estudios Avanzados en Zonas Áridas (CEAZA),
LaSerena, Chile
5 CSIRO Chile Research Foundation, Santiago, Chile
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2476 Á.Salazar et al.
1 3
reproduce large-scale patterns of climate for specific vari-
ables and correct the wet bias identified in previous CMIP
generations for western South America (Rivera and Arnould
2020), Australian climate (Grose etal. 2020), precipitation
in North America (Akinsanola etal. 2020), the spatiotempo-
ral pattern of monsoon over India (Gusain etal. 2020), China
and East Asia (Xin etal. 2020), West Africa (Faye and Akin-
sanola 2022), the Mediterranean region (Cos etal. 2022)
and areas of Southeast Asia (Ge etal. 2021; Try etal. 2022).
However, the latest generation of models conforming to the
CMIP6 ensemble still has limitations in the representation
of some climate processes, and projections must be studied
considering their uncertainties. Sources of uncertainties in
GCMs are due to model resolution and physics, which can
affect the description of subgrid convective heat transfer that
is especially relevant in mountainous areas (Foley 2010;
Peng etal. 2022). Additional uncertainties can result from
unknown future human influences on the climate system
such as land-surface feedback changes from land use/cover
transitions, technological advances and population growth
(IPCC 2022). The complexity, multiplicity and nonlinear
nature of the processes and feedbacks that the climate sys-
tem contains, obstacles its faithful representation in GCMs
(Ghil 2020; Ghil etal. 2008; Knutti etal. 2008).
A significant number of models of CMIP6 also present
a new attribute not seen in the previous CMIPs ensembles
as they likely overestimate equilibrium climate sensitiv-
ity (Tokarska etal. 2020). Equilibrium climate sensitivity
(ECS) is a tractable manner to characterize the temperature
response of the Earth to a change in CO2 forcing, which
depends on several feedback processes such as those associ-
ated with water vapor, lapse rate, surface albedo and clouds
(Knutti and Rugenstein 2015). The ECS of the CMIP5 mod-
els varied from 2.1 to 4.5°C; and the IPCC AR5 report in
2013 estimated that it likely ranges from 1.5 to 4.5°C (IPCC
2013). However, the ECS of some of the new CMIP6 GCMs
presents an ECS greater than 5°C, which can lead the mod-
els to project a warming that is greater than expected based
on multiple lines of evidence (IPCC 2022). This warming
can be traced to a positive net cloud feedback that is larger
in CMIP6 compared to CMIP5 by 20% (IPCC 2022). The
critical question is whether future warming projections of
such models are realistic or current climate assessments
need to recalibrate the raw ensemble or select a subset of
low-sensitive models (Tokarska etal. 2020). In our study,
we present all available results from CMIP6 GCMs, yet we
track those highly sensitive models detected by recent stud-
ies (Scafetta 2022; Tokarska etal. 2020).
The investigation of Chile’s future climate is of great
interest. First, it strides along ~ 4000km alongside the west
coast of South America and therefore presents a set of dis-
tinct climate zones with a marked north–south precipitation
gradient ranging from the hyper-arid Atacama Desert in the
north to polar climate near Antarctica. Mediterranean and
Temperate climates extend between these extremely dry/hot
and wet/cold climates (Beck etal. 2018). The presence of the
Andes Mountains adds additional complexity to the regional
climate system with elevations reaching up to ~ 7000m a.s.l.
The Andes produces a strong orographic enhancement of
synoptic-scale precipitation upstream of the mountains (Gar-
reaud 2009; Garreaud etal. 2013; Massmann etal. 2017;
Viale and Garreaud 2014). In the Chilean Patagonia, this
enhancement can produce annual total precipitation as high
as ~ 6000mm and can decrease to less than 100mm within
100km east of the Andes (Garreaud 2009; Viale etal. 2019).
These features challenge the ability of coarse-resolution
climate models’ simulations to resolve local-scale charac-
teristics produced by orographic forcing. They also hinder
the ability of proper validation of such simulations due to a
scarce ground observational network, in particular over areas
of complex topography and extremely dry and wet climates
(Bozkurt etal. 2019). Therefore, it is necessary to evaluate
the performance of the new set of GCMs over these highly
heterogeneous areas not currently recognized as distinct cli-
matic zones by Intergovernmental Panel on Climate Change
reference subregions (Almazroui etal. 2021; Iturbide etal.
2020).
Second, Chile is a climate-change hotspot because it has
shown to be very sensitive to global change, with a drying
trend that is expected to continue in the coming future (Bois-
ier etal. 2018; Garreaud etal. 2020). A large proportion of
south-Central Chile has experienced a consistent decreas-
ing precipitation trend since the late 1970s that is attribut-
able both to natural climate variability (e.g., Pacific Decadal
Oscillation) and anthropogenic warming (Boisier etal. 2018,
2016; Quintana and Aceituno 2012). Since 2010, Central
Chile has registered precipitation deficits ranging from 25
to 45%, with impacts on the Andean snowpack and declines
up to 90% in river flow, reservoir volumes, and groundwater
levels (Garreaud etal. 2017). This trend is particularly rel-
evant for snow-dominated catchments, which accumulate the
effects of precipitation deficits caused by persistent drought
conditions and provide less water for people and ecosystems
(Alvarez-Garreton etal. 2021). The warmer and drier trend
is also affecting snow cover of northern Chile (Schauwecker
etal. 2023), glaciers mass loss across the country (Ayala
etal. 2020; Dussaillant etal. 2019; Feron etal. 2019; Pel-
licciotti etal. 2014; Vuille etal. 2018), and incrementing the
frequency and magnitude of dry season wildfires in Central
and South-Central Chile with catastrophic impacts over nat-
ural and rural areas (González etal. 2018; Urrutia-Jalabert
etal. 2018). Future projections of precipitation and tem-
perature using CMIP ensembles, particularly in the Andes,
project a scenario-dependent enhancement of the current
trends (Zazulie etal. 2018; Pabón-Caicedo etal. 2020). In
Central Chile, Bozkurt etal. (2018) used 19 GCMs from
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2477CMIP6 precipitation andtemperature projections forChile
1 3
CMIP5 adjusted with observations and predicted a drying
of ∼ − 3% (RCP2.6), ∼ − 30% (RCP8.5) and a warming of
∼ + 1.2 ◦C (RCP2.6), ∼ + 3.5 ◦C (RCP8.5) by the end of
the century, leading to a decrease in annual runoff of about
40% in the RPC8.5 scenario. More recently, Aguayo etal.
(2021) explored a set of GCMs from CMIP5 and CMIP6 in
combination to hydrological modelling. Their results project
an increase in the duration, hydrological deficit, and fre-
quency of severe droughts of varying duration towards the
2040–2070 period that agrees with previous studies (Araya-
Osses etal. 2020; Penalba and Rivera 2016). Mardones and
Garreaud (2020) also showed that the future temperature
change for scenario RCP8.5 is associated with an upward
shift of snow-rain transition of 400–600m, increasing the
risk of landslides and flashfloods along the foothills of the
subtropical Andes. The vast majority of these studies have
been directed to south Central Chile whereas results of cli-
mate projections for the extreme north and south of Chile
are fraught with uncertainties.
In this study, we analyze the climate projections over
Chile at the end of the century (2080–2099) with respect
to the historical period (1986–2014) in a set of 36 CMIP6
GCMs under four emission scenarios. We accounted for the
heterogeneity in climate characteristics by dividing Chile
into four distinct subregions: Northern Chile, Central Chile,
Northern Patagonia, and Southern Patagonia. Over each of
these subregions, we compare the historical simulations
against a suit of gridded observation datasets and further
analyze the distribution of the simulated mean annual pre-
cipitation and near-surface temperature across space and
time in the present and future climate.
2 Data andmethodology
2.1 Study area
Our study area comprises the coastal and continental exten-
sion of Chile from parallel 17.5°S to 56°S (~ 4250 km exten-
sion). Based on Iturbide etal. (2020), we redefined their
IPCC reference regions of south-western South America
and southern South America to new four subregions for
Chile: Northern Chile, Central Chile, Northern Patagonia,
and Southern Patagonia (Fig.1). These subregions present
distinct climatic features that are wide enough to assess
results from CMIP6 models. Northern Chile (17.5–29°S)
covers the Atacama Desert. This subregion is character-
ized by a hyper-arid climate defined by a large-scale sub-
sidence over the subtropical southeast Pacific Ocean and
low sea surface temperature off Chile and Perú (Garreaud
etal. 2010, 2009). Driest conditions occur near the coast
and low elevation zones (≤ 1000 m ASL) with increasing
summer precipitation (DJF) at higher elevations (> 3000 m
ASL) due to moisture transport from lowland areas east of
the Andes (Garreaud etal. 2003). Central Chile (29–40°S)
has a typical Mediterranean climate with stratiform winter
precipitation (JJA). Its climate is shaped by the subtropical
anticyclone and the storm track at midlatitudes (Garreaud
etal. 2009, 2017), with a marked meridional precipitation
gradient forced by mechanical lift leading to an orographic
precipitation enhancement by a factor 1.8 ± 0.3 from the
coast to the western Andean slopes between 33 and 44°S
(Viale and Garreaud 2015; Garreaud etal. 2017). As in the
previous subregion, Northern Patagonia’s (40–47°S) temper-
ate climate is influenced by the subtropical anticyclone and
the circumpolar ring of midlatitude westerlies intersecting
South America between 40 and 50°S (Garreaud and Acei-
tuno 2007). High continental precipitation rates of Northern
Patagonia subside at about parallel 47°S, where cooler polar
conditions influence climate, and the tundra biome begins
to dominate (Aguirre etal. 2021; Beck etal. 2018). In the
present study, this change is considered the start of Southern
Patagonia (47–56°S), where the polar climate is influenced
by the circumpolar low-pressure belt surrounding Antarctica
around 60°S and the seasonal displacement of the subtropi-
cal anticyclone. These features modulate eastward frontal
precipitation, resulting in greater total precipitation but at
lower rates than over the northern neighbor region. South-
ern Patagonia exhibits a distinct zonal asymmetry, with wet
conditions alongside the west coast and drier/cold conditions
towards the east (Fig.6a).
2.2 Data
In this study, we set as the reference climate the 1986–2014
period. For this period, we included monthly observational
data for precipitation and near-surface temperature from dif-
ferent sources (summarized in Table1). Based on the avail-
ability at the time of writing, we assessed projected monthly
mean precipitation and temperature using 36 CMIP6 Global
Climate Models (GCMs, listed in Table2). The focus was
directed to the subregional mean annual changes in the late-
century period of 2080–2099 relative to the reference period
for four future scenarios, namely SSP1-2.6 (SSP126), SSP2-
4.5 (SSP245), SSP3-7.0 (SSP370) and SSP5-8.5 (SSP585).
We considered only one member (r1i1p1f1), and all mod-
els were weighted equally. Using conservative remapping,
all models and observations were re-gridded to a common
1° × 1° lat/lon resolution.
2.3 Evaluation
For each subregion, we evaluated CMIP6 GCMs against
the arithmetic mean of observation datasets (ensemble
mean). Because observational temperature datasets were
restricted to land, we processed this field for land grid
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2478 Á.Salazar et al.
1 3
points only. On the contrary, precipitation was processed
for the entire domain of each subregion including land and
ocean. The spatiotemporal performance of individual mod-
els for precipitation and temperature was assessed using
a set of statistical metrics. We addressed the annual cycle
of GCMs using Fourier transform equations. The ampli-
tude and phase of annual cycle was estimated by fitting
the annual Fourier harmonic (Ding etal. 2023; Hu etal.
2022) to the monthly series that were previously smoothed
using spline cubic functions. To appraise how well the
Fourier transform effectively describes the seasonal cycle
of the monthly series, we evaluated the performance of
Fourier predictions against the original precipitation and
temperature data across space and time for each of the
studied subregions. Then we used the Fourier method to fit
all series (observations and GCMs) and used their trigono-
metric properties to compare amplitude and climatological
peak month. The mean bias error MBE was calculated as
the mean distance between the climatological normal of
models and observations in units of the observed mean
field (Willmott 1982). The normalized root mean square
error NRMSE was calculated after normalizing the root
mean square error by the observations, and it was chosen
because it is more suitable when values differ in order of
magnitude (Guo etal. 2021). A smaller value of MBE
and NRMSE reflects a closer fit to observations for the
respective GCM. The equations of MBE and NRMSE are
given as follows:
Fig. 1 a Study area divided in
four subregions and b cor-
responding mean elevation by
latitude
1000 2000 3000 4000
80°W 75°W70°W65°W
55°
S
50°
S
45°
S
40°
S
35°
S
30°
S
25°
S
20°
S
55°S
50°S
45°
S
40°S
35°
S
30°S
25°S
20°S
0
3500
>5000
Elevation (m)
Northern
Chile
Central
Chile
Northern
Patagonia
Southern
Patagonia
Mean elevation (m)
a)
b)
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2479CMIP6 precipitation andtemperature projections forChile
1 3
yn
represents the simulated data for each climatological nor-
mal,
y
is the mean of simulated data,
on
is the observed value
of each climatological normal,
o
is the mean of observed
data.
N
is the time length of the annual cycle in months.
We applied Probability Density Functions (PDFs)
to evaluate the spatiotemporal distribution of monthly
precipitation and temperature for all observations and
models during the historical period. PDFs for each sub-
region were calculated by sorting all monthly grid points
in space (latitude and longitude) and time (months) and
partitioning these grid points into 50 bins. In Sect.3.1.1
we present PDFs for all observations grouped by ranges
of precipitation and temperature. In the Supplementary
Material, Figs. S1 and S2, we include all CMIP6 indi-
vidual models and their ensemble.
(1)
MBE
=
N
∑
n=1
(yn−on)
,
(2)
NRMSE
=




N
n=1on−yn2

N
n=1
o
n
2
,
2.4 Model ranking
Early evidence has suggested that among the CMIP ensem-
ble no one model is “best” for all variables and subregions
(Lambert and Boer 2001). Combining results of multi-
ple models (so-called, multi-model ensemble approach)
increases the skill, reliability and forecast consistency of
results compared to the solution of individual GCMs (Ge
etal. 2021; Kurniadi etal. 2022). The ensemble approach
also allows to quantify the uncertainty of the future climate
probabilistically (Tebaldi and Knutti 2007), and uncertain-
ties can further be reduced by selecting the best-performed
models of the multi-model ensemble (MME). Yet, models
composing the MME need to be carefully chosen as recent
evaluations of CMIP6 GCMs have identified the ‘hot model
problem’, resulting in a projected warming that might be
larger than supported by evidence (see Hausfather etal. 2022
and references therein). The cause of projected hotter tem-
peratures in CMIP6 is under investigation, but it might be
related to an overestimated cloud feedback, among other fac-
tors (e.g., Gettelman etal. 2019). This can introduce biases
in the MME toward high-temperature values (Liang etal.
Table 1 Gridded observations used in this study
Variable Dataset Method Resolution (°) Source
Precipitation CHIRPS v2.0 (Climate Hazards
group Infrared Precipitation with
Stations)
Satellite + Gauge 0.05 https:// www. chc. ucsb. edu/ data/ chirps
CMAP v2108 (CPC Merged Analy-
sis of Precipitation)
Satellite + Gauge 2.5 https:// psl. noaa. gov/ data/ gridd ed/
data. cmap. html
CR2 (Center for Climate and Resil-
ience Research)
Statistical downscaling from ERA-
Interim
0.05 https:// www. cr2. cl/ datos- produ ctos-
grill ados/
CRU v4.05 (Climate Research Unit) Gauge-analysis 0.5 https:// cruda ta. uea. ac. uk/ cru/ data/ hrg/
cru_ ts_4. 05/
GPCC v2020 (Global Precipitation
Climatology Centre)
Gauge-analysis 0.5 https:// clima tedat aguide. ucar. edu/
clima te- data/ gpcc- global- preci pitat
ion- clima tology- centre
GPCP v3.2 (Global Precipitation
Climatology Project)
Satellite + Gauge 0.5 https:// disc. gsfc. nasa. gov/ datas ets/
GPCPM ON_3. 2/ summa ry
PERSIANN (PERSIANN-CDR) Satellite + Artificial Neural net-
works
0.25 https:// clima tedat aguide. ucar. edu/
clima te- data/ persi ann- cdr- preci pitat
ion- estim ation- remot ely- sensed-
infor mation- using- artifi cial
University of Delaware V5.01 Gauge 0.5 http:// resea rch. jisao. washi ngton. edu/
data_ sets/ ud/
Temperature CR2 v2.0 (Center for Climate and
Resilience Research)
Satellite + ERA-Interim 0.05 https:// www. cr2. cl/ datos- produ ctos-
grill ados/
CRU v4.05 (Climate Research Unit) Gauge-analysis 0.5 https:// cruda ta. uea. ac. uk/ cru/ data/ hrg/
cru_ ts_4. 05/
University of Delaware V5.01 Gauge 0.5 http:// clima te. geog. udel. edu/ ~clima te/
html_ pages/ downl oad. html
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2480 Á.Salazar et al.
1 3
2020). In this study, we document the late-century changes
in precipitation and temperature of GCMs identifying the
‘hot models’, i.e. those models showing equilibrium cli-
mate sensitivity (ECS) values above the IPCC AR5 likely
range of 1.5–4.5°C (Scafetta 2022; Tokarska etal. 2020).
However, we do not discard these models in our analysis
and present all available projections. We ranked all GCMs
performance in space and time using the pattern correlation
coefficient (PCC) and the Taylor skill score (TSS, Taylor
2001), respectively. PCC (centered) measures the similarity
Table 2 List of CMIP6 models used in the study
# Model Institution and Country Resolution (lat. x lon)
1 ACCESS-CM2 Australian Community Climate and Earth System Simulator (ACCESS), Australia 1.3° × 1.9°
2 ACCESS-ESM1-5 Australian Community Climate and Earth System Simulator (ACCESS), Australia 1.2° × 1.9°
3 AWI-CM-1-1-MR Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Germany 0.9° × 0.9°
4 BCC-CSM2-MR Beijing Climate Center, Beijing, China 1.1° × 1.1°
5 CAMS-CSM1-0 Chinese Academy of Meteorological Sciences, Beijing, China 1.1° × 1.1°
6 CAS-ESM2-0 Chinese Academy of Sciences, Beijing, China 1.4° × 1.4°
7 CESM2-WACCM National Center for Atmospheric Research, Boulder, USA 0.9° × 1.3°
8 CIESM Department of Earth System Science, Tsinghua University, China 1° × 1°
9 CMCC-CM2-SR5 Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy 0.9° × 1.3°
10 CMCC-ESM2 Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy 0.9° × 1.3°
11 CanESM5 Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change
Canada, BC, Canada
2.8° × 2.8°
12 E3SM-1-1 Lawrence Livermore National Laboratory, Livermore, USA 1° × 1°
13 EC-Earth3 Consortium of various institutions from Spain, Italy, Denmark, Finland, Germany, Ireland,
Portugal, Netherlands, Norway, the United Kingdom, Belgium, and Sweden
0.7° × 0.7°
14 EC-Earth3-AerChem Consortium of various institutions from Spain, Italy, Denmark, Finland, Germany, Ireland,
Portugal, Netherlands, Norway, the United Kingdom, Belgium, and Sweden
0.7° × 0.7°
15 EC-Earth3-CC Consortium of various institutions from Spain, Italy, Denmark, Finland, Germany, Ireland,
Portugal, Netherlands, Norway, the United Kingdom, Belgium, and Sweden
0.7° × 0.7°
16 EC-Earth3-Veg Consortium of various institutions from Spain, Italy, Denmark, Finland, Germany, Ireland,
Portugal, Netherlands, Norway, the United Kingdom, Belgium, and Sweden
0.7° × 0.7°
17 EC-Earth3-Veg-LR Consortium of various institutions from Spain, Italy, Denmark, Finland, Germany, Ireland,
Portugal, Netherlands, Norway, the United Kingdom, Belgium, and Sweden
1.1° × 1.1°
18 FGOALS-f3-L Chinese Academy of Sciences, Beijing, China 1° × 1.3°
19 FGOALS-g3 Chinese Academy of Sciences, Beijing, China 2.3° × 2°
20 FIO-ESM-2–0 First Institute of Oceanography, Ministry of Natural Resources (FIO), China 0.9° × 1.3°
21 GFDL-CM4 National Oceanic and Atmospheric Administration, GFDL, Princeton, USA 1° × 1.3°
22 GFDL-ESM4 National Oceanic and Atmospheric Administration, GFDL, Princeton, USA 1° × 1.3°
23 IITM-ESM Centre for Climate Change Research, Indian Institute of Tropical Meteorology, India 1.9° × 1.9°
24 INM-CM4-8 Institute for Numerical Mathematics, Russian Academy of Science, Moscow, Russia 1.5° × 2°
25 INM-CM5-0 Institute for Numerical Mathematics, Russian Academy of Science, Moscow, Russia 1.5° × 2°
26 IPSL-CM5A2-INCA Institut Pierre Simon Laplace, Paris, France 2° × 2°
27 IPSL-CM6A-LR Institut Pierre Simon Laplace, Paris, France 1.3° × 2.5°
28 KACE-1–0-G National Institute of Meteorological Sciences/Korea Meteorological Administration (NIMS-
KMA), South Korea
1.9° × 1.3°
29 MIROC6 Japan Agency for Marine‐Earth Science and Technology, Atmosphere and Ocean Research
Institute, National Institute for Environmental Studies, and RIKEN Center for Computa-
tional Science, Japan
1.4° × 1.4°
30 MPI-ESM1-2-HR Max Planck Institute for Meteorology, Germany 0.9° × 0.9°
31 MPI-ESM1-2-LR Max Planck Institute for Meteorology, Germany 1.9° × 1.9°
32 MRI-ESM2-0 Meteorological Research Institute, Tsukuba, Japan 1.1° × 1.1°
33 NESM3 Nanjing University of Information Science and Technology, Nanjing, China 1.9° × 1.9°
34 NorESM2-LM NorESM Climate modeling Consortium, Norway 1.9° × 2.5°
35 NorESM2-MM NorESM Climate modeling Consortium, Norway 0.9° × 1.3°
36 TaiESM1 Research Center for Environmental Changes, Academia Sinica, Taiwan 0.9° × 1.3°
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2481CMIP6 precipitation andtemperature projections forChile
1 3
of two variables. It is computed as the Pearson correlation
applied to each pair of grid points of the simulated and
observed fields to show how well the observed spatial pat-
tern is captured by simulations (Rivera and Arnould 2020;
Shiferaw etal. 2018). TSS ranks GCMs based on how well
they replicate the annual cycle of observed fields and has
been successfully applied to account for GCMs skill in a
variety of studies (Guo etal. 2021; Lun etal. 2021; Ngoma
etal. 2021; Xin etal. 2020). The equations to calculate PCC
and TSS are given as follows:
where
ym
and
om
are the simulated and observed data at the
mth grid point, respectively.
y
and
o
represent the mean
value of simulated and observed data across
M
total of grid
points.
where
Rm
is the correlation of the annual cycle for the
reference period between each GCM and the observation
ensemble,
𝜎m
and
𝜎o
are the standard deviations of simulated
and observed patterns of the annual cycle, respectively.
R0
is the maximum correlation attainable, set as 0.999. TSS
approaches unity as the model spatial variance is closer to
the observed variance, and
Rm
approximates
R0
.
After selecting the top 5 models from both PCC and TSS
ranking list for each subregion, the final model ranking was
done, identifying the ‘hot models’ informed by Tokarska
etal. (2020) and Scafetta (2022). We finally plotted annual
mean precipitation change against temperature change of
the late-century period (2080–2099) relative to the refer-
ence period (1986–2014). This gave us a set of models that
are closer to observations, unbiased towards warming, and
represent enough ranges of uncertainties in future climate
changes.
3 Results anddiscussion
3.1 Annual cycle
3.1.1 Current climate fromtheobservation ensemble
Figure2 shows the spatially averaged annual precipitation
cycle for all models and the observation ensemble. Observa-
tions illustrate the aridity gradient with decreasing latitude
in continental Chile. The arid Northern Chile subregion
(3)
PCC
=
M
m=1ym−yom−−o

M
m=1

ym−y

2

M
m=1

om−o

2

1
2
,
(4)
TSS
=
41+Rm
2

𝜎m
𝜎
o
+𝜎o
𝜎
m
2

1+R0

2
,
receives a median precipitation of 0.16 (Inter-Quartile range,
IQR = 0.18) mm/day, most of which falling in the months of
December, January, and February, with amounts greater than
0.33 mm/day (75th percentile of the annual cycle). Further
south, winter precipitation dominates the annual cycle of
Central Chile and Northern Patagonia, where the months
of May, June, and July make the largest contribution to the
annual precipitation. However, the magnitude of precipita-
tion is greater in Northern Patagonia, which receives about
three times more precipitation than its northern neighbor
[2.67 (IQR = 1.73) vs. 0.91 (IQR = 1.39) mm/day]. In South-
ern Patagonia, precipitation abounds all year around, with
a median of 2.79 (IQR = 0.25) mm/day. Here, maximum
precipitation occurs during March, April, and May, which
receive more than the 75th percentile (2.91 mm/day) of pre-
cipitation during the annual cycle (Fig.2d).
Figure3 evidences that the annual mean temperature
ranges from 11.32°C in Northern Chile to 6.23°C in South-
ern Patagonia. During December, January, and February,
when the increased insolation in summer warms the land
in all subregions, mean temperature varies from 14.26°C
in Northern Chile to 9.57°C in Southern Patagonia. North-
ern Chile exhibits slightly lower temperatures in the austral
summer than Central Chile (− 1.67°C), which may be due
to much the higher mean elevation of this region (Fig.1).
Besides the more poleward position of Northern and South-
ern Patagonia with respect to northern and Central Chile,
lower temperature over this region is also influenced by the
summer increase in onshore moisture transport described
by Garreaud etal. (2013), which advent cool air over land.
During the winter months, the mean temperature varies from
7.95 to 2.16°C between the Northernmost and the south-
ernmost regions. As illustrated in Fig.7, while observations
indicate an absence of a west–east gradient for temperature
in Patagonia, there is a pronounced meridional asymmetry in
precipitation, with peak values occurring in the western side
of Southern Patagonia. Here, a band of maximum annual
precipitation stretches between parallel 47°S and 55°S and
between meridians 72.5°W and 77°W. Precipitation rapidly
decreases towards the east to a minimum of 0.57 mm/day in
Tierra del Fuego.
To evaluate the dispersion between the observations
over each subregion we used Probability Density Func-
tions (PDFs). These are presented as density boxplots for
a set of ranges of precipitation and temperature in Figs.4
and 5, respectively (original PDFs can be seen in the Sup-
plementary Material). We detected substantial differences
among observations in the distribution of precipitation in
the wetter subregions of Northern and Southern Patagonia.
The frequency density of the months with low precipita-
tion (≤ 2mm/day) is much greater in CMAP than in the
other products. PERSIANN exhibits the highest frequency
of months with 2–4mm/day for the same subregions and in
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2482 Á.Salazar et al.
1 3
Southern Patagonia it also shows the highest frequency for
months with intensity ≥ 4mm/day (Fig.4b, c). CMAP and
PERSIANN are the datasets showing the highest frequency
of low-intensity months (≤ 2mm/day) for Central Chile,
although in general, the observations are relatively more
homogeneous in the representation of low and medium-
intensity precipitation months in this semi-arid region than
in Patagonia (Fig.4b). In Northern Chile, where the monthly
precipitation amount rarely exceeds 2 mm/day, CHIRPS and
CMAP showed the greatest frequency and greatest IQR of
the months with low intensity (< 2mm/day).
The monthly distribution of temperature presents more
agreement among observations than the precipitation in
all regions (Fig.5). In the two northernmost regions the
UDelaware dataset shows the greatest frequency for the
months with lowest temperatures (< 5°C) and the smallest
frequency for the rest of the months with respect to the other
two datasets. Torrez-Rodriguez etal. (2023) showed that
CR2 is substantially warmer than CRU and UDelaware over
high mountains between 25 and 35°S dataset. Our results
suggest that the CR2 dataset exhibits the highest frequency
of the temperature values in the range of 10–15°C (Fig.5a),
but in terms of the PDF shape the CR2 is very similar to the
CRU in all subregions (Fig. S2).
3.1.2 CMIP6 ensemble versus observations
In Fig.6, we summarize the results of subregional differ-
ences in the annual cycle between CMIP6 models and the
observation ensemble for the period 1986–2014 using the
difference in annual Fourier harmonic amplitude (Fig.6a),
the difference in the climatological peak month (Fig.6b),
MBE (Fig.6c), and NRMSE (Fig.6d). The corresponding
values for each individual model are provided in Supplemen-
tary Material (Tables S1 and S2). First, we verified that the
annual Fourier harmonic was able to successfully capture
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Precipitation (mm/day)
a) Northern Chile
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1
2
3
4
5
Precipitation (mm/day)
b) Central Chile
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2
3
4
5
6
7
8
Precipitation (mm/day)
c) Northern Patagonia
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
Precipitation (mm/day)
d) Southern Patagonia
ACCESS-CM2
ACCESS-ESM1-5
AWI-CM-1-1-MR
BCC-CSM2-MR
CAMS-CSM1-0
CAS-ESM2-0
CESM2-WACCM
CIESM
CMCC-CM2-SR5
CMCC-ESM2
CanESM5
E3SM-1-1
EC-Earth3-AerChem
EC-Earth3-CC
EC-Earth3-Veg-LR
EC-Earth3-Veg
EC-Earth3
FGOALS-f3-L
FGOALS-g3
FIO-ESM-2-0
GFDL-CM4
GFDL-ESM4
IITM-ESM
INM-CM4-8
INM-CM5-0
IPSL-CM5A2-INCA
IPSL-CM6A-LR
KACE-1-0-G
MIROC6
MPI-ESM1-2-HR
MPI-ESM1-2-LR
MRI-ESM2-0
NESM3
NorESM2-LM
NorESM2-MM
TaiESM1
CMIP6 Ens.
OBS. Ens.
Fig. 2 Annual precipitation cycle in CMIP6 models and the observa-
tion ensemble dataset for Chile. Each point in the plot is the monthly
average of the dataset across the reference period (1986–2014) and
averaged across each subregion. Blue and black lines show the annual
cycle of the CMIP6 ensemble (CMIP6 Ens.) and Observation Ensem-
ble (Obs. Ens.), respectively
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2483CMIP6 precipitation andtemperature projections forChile
1 3
the observed annual cycle of precipitation and temperature.
In case of precipitation, over the most of Chile the corre-
sponding explained variance is greater than 90% (Fig. S3).
A relatively low explained variance (less than 65%) over a
part of Northern Chile and Southern Patagonia is likely due
to weak amplitude of the observed annual cycle (Fig.2a,
d). For temperature, the annual Fourier harmonic explains
more than 98% of the annual cycle over the entire Chile (Fig.
S4). These results give us reliable amplitude and phase to
evaluate the performance of CMIP6 models for the analysis
of the annual cycle.
Figure6a evidences a general overestimation (under-
estimation) of the amplitude of annual cycle of precipita-
tion (temperature). In Northern Chile, CMIP6 showed the
greatest median difference in the precipitation amplitude
(325% or 0.71 mm/day), followed by Southern Patagonia
(63% or 0.16 mm/day), Northern Patagonia (49% or 0.61
mm/day) and Central Chile (19% or 0.17 mm/day,). Tem-
perature amplitude differences revealed differences for all
subregions that ranged from − 0.8°C in Northern Chile
to − 1.4°C in Southern Patagonia. Figure6b shows that the
timing of the climatological peak month for precipitation is
generally delayed by up to 21 days in Central Chile, 10 days
in Northern Patagonia, 8 days in Southern Patagonia, and 3
days in Northern Chile. However, Central Chile and South-
ern Patagonia showed a high inter-model variability, with
an IQR of 27 days and 74 days, respectively. The models
exhibited a delay also in the annual temperature cycle, with
a range of 10–15 days across the regions. The interquartile
range (IQR) was less than 6 days, indicating a weak inter-
model variability.
The magnitude of the average model bias is shown by
the mean bias error (MBE). This metric, however, needs
to be interpreted alongside NRMSE, given that the total
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
8
10
12
14
16
18
20
22
Temperature (°C)
a) Northern Chile
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
6
8
10
12
14
16
18
20
22
Temperature (°C)
b) Central Chile
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
4
6
8
10
12
14
16
Temperature (°C)
c) Northern Patagonia
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2
4
6
8
10
12
Temperature (°C)
d) Southern Patagonia
ACCESS-CM2
ACCESS-ESM1-5
AWI-CM-1-1-MR
BCC-CSM2-MR
CAMS-CSM1-0
CAS-ESM2-0
CESM2-WACCM
CIESM
CMCC-CM2-SR5
CMCC-ESM2
CanESM5
E3SM-1-1
EC-Earth3-AerChem
EC-Earth3-CC
EC-Earth3-Veg-LR
EC-Earth3-Veg
EC-Earth3
FGOALS-f3-L
FGOALS-g3
FIO-ESM-2-0
GFDL-CM4
GFDL-ESM4
IITM-ESM
INM-CM4-8
INM-CM5-0
IPSL-CM5A2-INCA
IPSL-CM6A-LR
KACE-1-0-G
MIROC6
MPI-ESM1-2-HR
MPI-ESM1-2-LR
MRI-ESM2-0
NESM3
NorESM2-LM
NorESM2-MM
TaiESM1
CMIP6 Ens.
OBS. Ens.
Fig. 3 Annual cycle of temperature in CMIP6 models and observa-
tion ensemble dataset for Chile. Each point in the plot is the monthly
average of the dataset across the reference period (1986–2014) and
averaged across each subregion. Blue and black lines show the annual
cycle of the CMIP6 ensemble (CMIP6 Ens.) and Observation Ensem-
ble (OBS. Ens.), respectively
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2484 Á.Salazar et al.
1 3
precipitation amount varies significantly among the differ-
ent subregions. The second lowest MBE occurs in North-
ern Chile (extremely arid) with a median of 0.44 mm/day.
However, this subregion shows the greatest relative error as
shown by the NRMSE with a value of 2.17 (205%). Cen-
tral Chile presented the lowest NRMSE of 0.44 (37%), fol-
lowed by Northern Patagonia with 0.46 (42%). Southern
Patagonia showed the greatest MBE of 1.66 mm/day and
the second largest NRMSE of 0.66 (65%). Northern and
Central Chile presented the highest MBE with 2.6°C and
2.1°C, respectively. This error decreases with latitude down
to 0.6°C in Southern Patagonia. However, projections of
GCMs are highly variable as shown by an IQR of 1.57°C
(Fig.3). NRMSE registered values less or equal to 0.2°C
across subregions.
3.2 Spatial pattern ofannual means: CMIP6
ensemble bias
The spatial bias of the annual mean for precipitation and
temperature of the CMIP6 ensemble is portrayed in Figs.7
and 8, respectively. CMIP6 precipitation shows a strong wet
bias reaching up to 6.8 mm/day in Southern Patagonia and
up to 5.8 mm/day in the southern tip of Northern Patago-
nia. A wet bias is present in Northern Chile, which shows a
maximum of 4.5 mm/day towards the high Andean Plateau
in Bolivia. Central Chile registered the lowest precipita-
tion bias for Chile with values less than 1 mm/day. Central
Chile is the subregion where CMIP6 best performs, as it
shows the best spatial and temporal fit with respect to the
observations (Fig.9). This result, though, does not extend
to near-surface temperature as it shows the second largest
positive bias with 7°C in high-elevation areas of the Andes
Mountains, certainly due to an unresolved topography by
coarse-resolutions CMIP6 models. This bias is only sur-
passed by 8°C in specific areas in the Atacama Desert in
Northern Chile. In the remaining subregions, temperature
bias ranges within ± 2°C, except for a slight cool bias at
about 47°S (Fig.8). Figure9 exhibits the latitudinal averages
of temperature and precipitation of the CMIP6 and obser-
vational ensembles. This figure displays the strong bias in
precipitation from parallel 43S (Northern Patagonia) with a
0-2 2-4 4-6 6-8 8-10 >10
Precipitation (mm/day)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Density
a) Northern Chile
Observations
CHIRPS
CMAP
CR2
CRU
GPCC
GPCP
PERSIANN
UDelaware
0-2 2-4 4-6 6-8 8-10 >10
Precipitation (mm/day)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Density
b) Central Chile
Observations
CHIRPS
CMAP
CR2
CRU
GPCC
GPCP
PERSIANN
UDelaware
0-2 2-4 4-6 6-8 8-10 >10
Precipitation (mm/day)
0.00
0.05
0.10
0.15
0.20
0.25
Density
c) Northern Patagonia
Observations
CHIRPS
CMAP
CR2
CRU
GPCC
GPCP
PERSIANN
UDelaware
0-2 2-4 4-6 6-8 8-10 >10
Precipitation (mm/day)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Density
d) Southern Patagonia
Observations
CMAP
CR2
CRU
GPCC
GPCP
PERSIANN
UDelaware
Fig. 4 Probability density functions (PDFs) of monthly precipitation
for specific intensity ranges over four subregions of Chile. To con-
struct the PDFs in each subregion, we separated all monthly precip-
itation over each latitude and longitude grid cell using 50 bins (see
original PDFs in Supplementary Material)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2485CMIP6 precipitation andtemperature projections forChile
1 3
maximum bias around parallel 49S (Southern Patagonia). It
reveals the great difficulty of CMIP6 models in replicating
the spatial pattern of precipitation in Patagonia. This region
is characterized by a reduced network of ground observa-
tions in space and time which impose challenges in validat-
ing climate models. For temperature, the strongest bias is
found in Northern Chile with a maximum at around parallel
30S along the Andes. Figure9 also replicates the propor-
tional change in temperature according to the climate change
scenario, with warming that increases from scenario SSP126
towards scenario SSP585.
We used the pattern correlation coefficient (PCC) to
evaluate the subregional spatial agreement between CMIP6
models and the observation ensemble over the annual mean
of precipitation and temperature across the reference period
(1986–2014). Results are shown in Table3 as the PCC aver-
age of precipitation and temperature for individual models.
Overall, Northern Chile showed the largest PCC across all
subregions with a median of 0.89 (IQR = 0.06). Here, 30
out of 36 models (83%) scored PCC > 0.8, suggesting that
CMIP6 simulations perform well in replicating the observed
spatial pattern of precipitation and temperature. On the other
hand, Southern Patagonia presented the lowest PCC of all
subregions with a median of 0.65 (IQR = 0.11). Only 1 out
of 36 CMIP6 models in this subregion scored a PCC > 0.8
(Table3). This result was triggered by a wet bias of more
than 6 mm/day near coastal areas (Fig.7). Northern Patago-
nia showed a median score of 0.81 (0.04), and 18 out of
36 models (50%) had a PCC > 0.8. Central Chile’s median
PCC was 0.78 (0.16), and 13 out of 36 models (36%) scored
a PCC > 0.8. Since Central Chile’s precipitation bias was
low, this score is attributable to the unsatisfactory simulation
of the annual spatial mean of temperature in the northern
extreme of this subregion (Fig.8c). For the entirety of Chile,
35 out of 36 models showed PCC > 0.8, and the best-per-
forming models on PCC scores were: GFDL-CM4, GFDL-
ESM4, EC-Earth3-Veg-LR, FGOALS-f3-L, and EC-Earth3-
CC. Grouped by model family, GFDL and EC-EARTH3
best described the spatial pattern of precipitation and tem-
perature. These results compare to the study of Rivera and
Arnauld (2020), who evaluated precipitation projections of
14 CMIP6 models for a region covering Central Chile and
Northern Patagonia in our research. Though the study of
Rivera and Arnauld (2020) is not directly comparable with
our study (they analyzed simulations of precipitation only
over the longest period, 1901–2014), they applied a similar
-10--5 -5-0 0-5 5-10 10-15 15-20 >20
Temperature (°C)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Density
a) Northern Chile
Observations
CR2
CRU
UDelaware
-10--5 -5-0 0-5 5-10 10-15 15-20 >20
Temperature (°C)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Density
b) Central Chile
Observations
CR2
CRU
UDelaware
-10--5 -5-0 0-5 5-10 10-15 15-20 >20
Temperature (°C)
0.00
0.02
0.04
0.06
0.08
0.10
Density
c) Northern Patagonia
Observations
CR2
CRU
UDelaware
-10--5 -5-0 0-5 5-10 10-15 15-20 >20
Temperature (°C)
0.00
0.02
0.04
0.06
0.08
0.10
Density
d) Southern Patagonia
Observations
CR2
CRU
UDelaware
Fig. 5 Same as Fig.4 but for monthly temperature (see original PDFs Supplementary Material)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2486 Á.Salazar et al.
1 3
methodology to evaluate the spatial pattern of CMIP6 pro-
jections. As in our study, Rivera and Arnauld (2020) found
that most models scored a PCC > 0.8 with a marked overes-
timation of precipitation.
3.3 Model ranking ofannual cycle
So far, we have thoroughly evaluated the spatiotemporal per-
formance of CMIP6 models across Chile. We finally summa-
rized the models’ performance by applying the Taylor Skill
Score metric (TSS) over the annual cycle, which helped us to
select the best-performing models for precipitation and tem-
perature. TSS results are displayed in Table4, with the five
best-performing models shown in bold for each of the four
subregions and for the entire Chile. Although models rep-
licated the precipitation pattern in Northern Chile, they all
strongly overestimated this variable during the rainy season
(December, January, and February). This bias is represented
on the TSS metric, which, averaged across models, was low-
est among all subregions, including the entire Chile (0.63).
Those models with the best performance in describing the
average annual precipitation cycle and temperature were:
CAS-ESM2-0, FGOALS-f3-L, ACCESS-CM2, KACE-1-
0-G, and MPI-ESM1-2-HR. The model ACCESS-CM2 is
identified as a ‘hot model’ by Tokarska etal. (2020) and
Scafetta (2022). The bias of these warm models is discussed
in Sect.3.4.
Central Chile scored the highest average TSS across
all subregions (0.90). The best-performing models were:
CanESM5, INM-CM4-8, CAMS-CSM1-0, IPSL-CM5A2-
INCA, and MPI-ESM1-2-HR. It is worth noting that
CanESM5 had the highest score among all CMIP6 mod-
els for Central Chile. This model was also identified as
the best performing in the study of Rivera and Arnould
(2020). However, it is a “hot model” and, as it will be
shown in Sect.3.4, presents the warmest temperature pro-
jection for the end of the century. Therefore, care should
be taken when considering this model for future tempera-
ture predictions in Central Chile. Another interesting fea-
ture of Central Chile is that the model ensemble scored the
sixth-best TSS score for the subregion (0.95).
Northern Patagonia scored Chile's second-highest
average TSS metric (0.87). The best-performing models
were: GFDL-CM4, FGOALS-f3-L, EC-Earth3-CC, EC-
Earth3, and IPSL-CM5A2-INCA. In Southern Patagonia,
the ensemble mean had the highest TSS (0.84), and the
best-performing models were: FGOALS-f3-L, MIROC6,
NorESM2-LM, FGOALS-g3, and MPI-ESM1-2-HR.
The best-performing models for Chile were: AWI-CM-1-
1-MR, NorESM2-LM, MPI-ESM1-2-HR, IPSL-CM5A2-
INCA, and IITM-ESM.
−4
−3
−2
−1
0
1
Amplitude difference
a)
Pr
Tas −50
−30
−10
10
30
50
70
90
110
130
Peak difference
b)
Pr
Tas
Northern
Chile
Central
Chile
Northern
Patagonia
Southern
Patagonia
0
1
2
3
4
5
6
MBE
c)
Pr
Tas
Northern
Chile
Central
Chile
Northern
Patagonia
Southern
Patagonia
0
1
2
3
4
NRMSE
d)
Pr
Tas
Fig. 6 Summary of metrics used to validate the annual cycle of
CMIP6 models against the observations for the four subregions for
the period 1986–2014: a amplitude difference, b peak month differ-
ence, c MBE and d NRSME. Amplitude difference and MBE for Pre-
cipitation (Pr) is in mm/day, while for temperature (Tas) it is in°C.
Peak difference is in days and NRMSE values are dimensionless
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2487CMIP6 precipitation andtemperature projections forChile
1 3
3.4 Future projections (2080–2099)
In this section, we analyze the changes in temperature and
precipitation at the end of the century with respect to the
historical period under four emission scenarios (Figs.10
and 11, respectively). The precipitation changes are spa-
tially consistent between CMIP6 models in Central Chile
and Northern Patagonia and become robust (at least 90% of
models agree on the sign of precipitation change) from sce-
nario SSP245 onwards, which shows a precipitation reduc-
tion of 10–20% (Fig.10). This reduction is much more sub-
stantial when increasing the strength of the anthropogenic
radiative forcing. In Central Chile, under scenario SSP585,
CMIP6 models project a mean reduction of 30–40% of
annual precipitation that is spatially consistent across 90%
of CMIP6 GCMs. Similarly to Almazroui etal. (2021),
we found that the changes tend to become stronger with
increasing radiative forcing, suggesting a potentially simple
proportional scaling. There is less inter-model agreement in
the projected precipitation change in the extremely dry and
wet subregions. In Northern Chile, the CMIP6 ensemble
forecasts up to a 20% decrease in mean annual precipitation.
However, this tendency is inconsistent across GCMs (non-
robust change) as well as across the scenarios as a decrease
is projected under scenarios SSP126, SSP245 and SSP370,
and a general non-robust increase in precipitation under
scenario SSP585 (Fig.10). The robust drying projected for
the Central Chile and Northern Patagonia extends to the
northern part of Southern Patagonia in scenarios SSP370
and SSP585, reaching up to 20% at around parallel 47°S. In
the southernmost portion of the latter subregion, the sign of
the changes varies amongst GCMs (see Fig.12) showing a
non-robust increase in the ensemble mean.
Our findings are in agreement with previous studies
focused on the future climate change in Chile in CMIP5
and CMIP6. For instance, Bozkurt etal. (2018) reported
70°W
17°S
29°S
40°S
47°S
56°S
a) Ensemble Obs.
70°W
17°S
29°S
40°S
47°S
56°S
b) Ensemble CMIP6
70°W
17°S
29°S
40°S
47°S
56°S
c) CMIP6 - OBS
0 1 2 3 4 5 6 7
mm/day
0 2 4 6 8 101 3 5 7 9
mm/day
Fig. 7 Spatial patterns of the annual mean precipitations for the
period 1986–2014: a displays the ensemble mean of the 8 observa-
tional datasets, b shows the ensemble mean of 36 CMIP6 models, and
c shows the CMIP6 mean bias (values ± 1 are shown in white). Units
for all maps are in mm/day. Note that the Y-axis shows the latitudinal
limits of each subregion considered in this study (Fig.1)
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2488 Á.Salazar et al.
1 3
a drying of up to ~ 30% over Central Chile using pro-
jections from CMIP5 by the end of the century. The
trends identified in CMIP5 are consistent with recent
CMIP6 models that project a robust drying over Medi-
terranean-type climate regions, including Central Chile
(Cook etal. 2020). When compared with the histori-
cal period, future changes in precipitation over Central
Chile are more significant than the baseline variability
under scenarios SSP370 and SSP585, and changes in
precipitation become temporarily and spatially robust
from mid-century onwards, reaching − 2mm/day com-
pared to the historical period (Almazroui etal. 2021).
CMIP6 projected changes in precipitation, especially in
Central Chile and Northern Patagonia, are related to a
change in the width and strength of the Hadley cell with
a poleward storm-track shift. This implies a southern
expansion of the band of subtropical subsidence, lead-
ing to enhanced mid-latitude tropospheric warming and
poleward shifts of the subtropical dry zone and increased
subtropical drought events documented since 1979 (Hu
etal. 2011; Huang etal. 2016). This change in general
circulation features is replicated by CMIP6 models, which
show a total annual-mean trend in the width of the Hadley
cells of 0.13° ± 0.02° per decade over 1970–2014 across
historical simulations (Xia etal. 2020) and that is 2–3
times larger in the Southern Hemisphere (SH) (Grise and
Davis 2020). It’s been suggested that natural SST vari-
ability primarily related to El Niño Southern Oscillation
(ENSO) and the Pacific Decadal Oscillation (PDO) are
the main factors explaining the observed shift patterns
(Allen and Kovilakam 2017). PDO also contributes about
half of the observed precipitation trend in Central Chile
(Boisier etal. 2016), which is expected to be reinforced
in the future by anthropogenic forcing. By consequence,
Central Chile will experience the strongest increments in
meteorological droughts by the end of the century with
70°W
17°S
29°S
40°S
47°S
56°S
a) Ensemble Obs.
70°W
17°S
29°S
40°S
47°S
56°S
b) Ensemble CMIP6
70°W
17°S
29°S
40°S
47°S
56°S
c) CMIP6 - OBS
0 4 8 12 16 20 24 28
mm/day
−8 −6 −4 −2 0 2 4 6 8
°C
Fig. 8 Spatial patterns of the annual mean temperature for the period
1986–2014: a displays the ensemble mean of the 3 observational
datasets, b shows the ensemble mean of 36 CMIP6 models, and c
shows the CMIP6 mean bias. Units for all maps are in°C. Note that
the Y-axis shows the latitudinal limits of each subregion considered
in this study (Fig.1)
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2489CMIP6 precipitation andtemperature projections forChile
1 3
Fig. 9 Mean annual precipita-
tion and temperature versus
latitude across Chile. Verti-
cal grey dashed lines show
the latitudinal limits of each
subregion. Values are averaged
across longitude and show the
latitudinal variations for the
observation ensemble for the
period 1986–2014 (Observa-
tions), CMIP6 historical ensem-
ble for the period 1986–2014
(Historical), and projections
for four emission scenarios
(SSP126, SSP245, SSP370,
and SSP585) for the period
2080–2099. Shaded areas
around lines represent the 95%
confidence interval around the
mean of latitude grid points
−60−55−50−45−40−35−30−25−20
Latitude
4
6
8
10
12
14
16
18
20
22
Temperature (°C)
Northern
Chile Central
Chile Northern
Patagonia Southern
Patagonia
−6
0
−55−50−45−40−35−30−25−20
Latitude
0
1
2
3
4
5
6
7
8
Precipitation (mm/day)
Northern
Chile Central
Chile Northern
Patagonia Southern
Patagonia
Scenario
Observations
Historical
SSP126
SSP245
SSP370
SSP585
Scenario
Observations
Historical
SSP126
SSP245
SSP370
SSP585
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2490 Á.Salazar et al.
1 3
high CMIP6 intermodel agreement (Ukkola etal. 2020).
The intensity of these drought events is much stronger and
more robust in CMIP6 compared to CMIP5. This trend
also extends to Northern Patagonia, which is projected to
be affected by an increase in the duration, hydrological
deficit, and frequency of severe droughts (Aguayo etal.
2021; Garreaud 2018).
Table 3 PCC ranking summary of CMIP6 models for Chile
Values show the regional average of PCC for precipitation and tem-
perature compared against the ensemble of observations from 1986
to 2014. Bold values show the five best-performed models for North-
ern Chile (N. CHL), Central Chile (C. CHL), Northern Patagonia (N.
Pat), Southern Patagonia (S. Pat), and the entire domain (Chile)
a Models with ECS values above the IPCC AR5 likely range (1.5–
4.5°C). ‘Hot models’ were identified from Tokarska etal. (2020) and
Scafetta (2022)
Model N. CHL C. CHL N. Pat S. Pat Chile
ACCESS-CM2a0.840 0.679 0.819 0.631 0.865
ACCESS-ESM1-5 0.800 0.660 0.838 0.739 0.890
AWI-CM-1-1-MR 0.839 0.883 0.810 0.646 0.881
BCC-CSM2-MR 0.846 0.734 0.790 0.659 0.821
CAMS-CSM1-0 0.873 0.844 0.796 0.599 0.867
CAS-ESM2-0 0.895 0.747 0.567 0.444 0.797
CESM2-WACCMa0.912 0.765 0.776 0.746 0.861
CIESMa0.920 0.789 0.802 0.721 0.889
CMCC-CM2-SR5 0.902 0.653 0.810 0.713 0.842
CMCC-ESM2 0.904 0.697 0.820 0.701 0.853
CanESM5a0.774 0.742 0.720 0.547 0.826
E3SM-1-1a0.894 0.831 0.774 0.669 0.892
EC-Earth3-AerChem 0.896 0.877 0.842 0.602 0.892
EC-Earth3-CC 0.894 0.866 0.843 0.618 0.893
EC-Earth3-Veg-LR 0.891 0.852 0.847 0.633 0.902
EC-Earth3-Veg 0.894 0.871 0.841 0.616 0.893
EC-Earth3 0.891 0.869 0.838 0.612 0.890
FGOALS-f3-L 0.917 0.856 0.757 0.556 0.895
FGOALS-g3 0.891 0.800 0.780 0.668 0.846
FIO-ESM-2–0 0.914 0.769 0.817 0.743 0.877
GFDL-CM4 0.947 0.905 0.829 0.649 0.923
GFDL-ESM4 0.943 0.903 0.800 0.647 0.917
IITM-ESM 0.849 0.781 0.793 0.554 0.888
INM-CM4-8 0.785 0.582 0.767 0.746 0.863
INM-CM5-0 0.787 0.600 0.786 0.734 0.866
IPSL-CM5A2-INCA 0.848 0.469 0.508 0.634 0.841
IPSL-CM6A-LRa0.878 0.781 0.740 0.601 0.873
KACE-1–0-G 0.783 0.682 0.733 0.595 0.846
MIROC6 0.857 0.419 0.827 0.722 0.850
MPI-ESM1-2-HR 0.875 0.876 0.824 0.609 0.893
MPI-ESM1-2-LR 0.843 0.726 0.817 0.631 0.882
MRI-ESM2-0 0.938 0.839 0.805 0.604 0.890
NESM3a0.856 0.701 0.810 0.664 0.868
NorESM2-LM 0.718 0.587 0.744 0.806 0.815
NorESM2-MM 0.881 0.787 0.819 0.745 0.881
TaiESM1 0.911 0.780 0.808 0.689 0.870
Table 4 TSS ranking summary of CMIP6 models for Chile
Values show the regional average of TSS for the precipitation and
temperature annual cycles compared against the ensemble of observa-
tions from 1986 to 2014. Bold values show the five best-performed
models for Northern Chile (N. CHL), Central Chile (C. CHL), North-
ern Patagonia (N. Pat), Southern Patagonia (S. Pat), and the entire
domain (Chile)
a Models with ECS values above the IPCC AR5 likely range (1.5–
4.5°C). ‘Hot models’ were identified from Tokarska etal. (2020) and
Scafetta (2022)
Model N. CHL C. CHL N. Pat S. Pat Chile
ACCESS-CM2a0.811 0.865 0.833 0.771 0.816
ACCESS-ESM1-5 0.571 0.885 0.775 0.715 0.761
AWI-CM-1-1-MR 0.762 0.937 0.904 0.767 0.919
BCC-CSM2-MR 0.551 0.876 0.862 0.411 0.603
CAMS-CSM1-0 0.571 0.967 0.825 0.533 0.842
CAS-ESM2-0 0.864 0.837 0.88 0.778 0.809
CESM2-WACCMa0.615 0.847 0.811 0.568 0.863
CIESMa0.618 0.842 0.788 0.661 0.756
CMCC-CM2-SR5 0.53 0.826 0.894 0.639 0.642
CMCC-ESM2 0.521 0.871 0.869 0.573 0.625
CanESM5a0.56 0.979 0.915 0.71 0.648
E3SM-1-1a0.559 0.956 0.893 0.692 0.702
EC-Earth3 0.652 0.925 0.922 0.663 0.81
EC-Earth3-AerChem 0.669 0.923 0.901 0.726 0.833
EC-Earth3-CC 0.633 0.921 0.923 0.684 0.844
EC-Earth3-Veg 0.646 0.924 0.896 0.702 0.85
EC-Earth3-Veg-LR 0.625 0.891 0.867 0.642 0.82
FGOALS-f3-L 0.855 0.836 0.935 0.832 0.866
FGOALS-g3 0.515 0.935 0.921 0.792 0.607
FIO-ESM-2–0 0.521 0.88 0.833 0.774 0.753
GFDL-CM4 0.625 0.948 0.939 0.748 0.823
GFDL-ESM4 0.605 0.91 0.91 0.76 0.831
IITM-ESM 0.582 0.779 0.896 0.684 0.872
INM-CM4-8 0.604 0.975 0.811 0.616 0.831
INM-CM5-0 0.572 0.91 0.813 0.61 0.779
IPSL-CM5A2-INCA 0.72 0.956 0.921 0.708 0.884
IPSL-CM6A-LRa0.658 0.836 0.909 0.652 0.794
KACE-1–0-G 0.807 0.891 0.864 0.673 0.832
MIROC6 0.584 0.817 0.782 0.822 0.803
MPI-ESM1-2-HR 0.798 0.952 0.879 0.779 0.89
MPI-ESM1-2-LR 0.575 0.936 0.819 0.752 0.811
MRI-ESM2-0 0.564 0.782 0.867 0.634 0.756
NESM3a0.543 0.922 0.74 0.459 0.777
NorESM2-LM 0.603 0.912 0.882 0.808 0.918
NorESM2-MM 0.627 0.95 0.869 0.701 0.856
TaiESM1 0.541 0.872 0.821 0.684 0.797
Ensemble 0.611 0.95 0.902 0.84 0.867
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2491CMIP6 precipitation andtemperature projections forChile
1 3
Figure11 shows the temperature projections over Chile
from the CMIP6 ensemble. In the case of temperature, we
evaluated the strength of changes in sign and magnitude by
identifying those grid cells where the projected ensemble
mean change is at least twice the standard deviation of the
reference period (Almazroui etal. 2021; Scheff and Frier-
son 2012). In contrast to precipitation, temperature changes
incrementally in all models, all subregions and all emis-
sion scenarios. CMIP6 projects a mean annual temperature
increase between 0 and 2°C in SSP126, 1–3°C in SSP245,
1–5°C in SSP370, and 2–6°C in SSP585 by the end of the
century. Amongst all subregions, Northern Chile displays
the greatest increments in temperature ranging from 1–2°C
in the lowest emission scenario to 4–6° in the highest emis-
sion scenario, with the maximum change occurring in high
mountains. Consistent changes in temperature are present in
only a few grid points in SSP370 and over the Andes range
in SSP585. The second warmer projection occurs in Central
Chile from about 1–1.5°C in SSP126 to 4–5°C in SSP585.
The strongest increments in mean annual temperature are
also presented across the Andes range with a 4–5°C greater
temperature than the reference period. The magnitude of
these changes is strong only in the southern portion of the
subregion and alongside the coast in the intermediate emis-
sion scenario SSP370 and more explicit in the high emis-
sion scenario SSP585. This pattern is also present in North-
ern Patagonia, where pronounced changes in temperature
become visible in its meridional extreme in emission sce-
nario SSP370 and widespread in the high emission scenario
SSP585. In this scenario, CMIP6 projects a marked increase
up to 2–3°C across all the subregions, with the most sig-
nificant changes alongside the Andes range. Temperature
changes in Southern Patagonia become strong in scenario
SSP370 with increments of 2–3°C and 3–4°C change over
the Andes in the high emission scenario SSP585 (Fig.11).
The projected increase in Andean temperature across sub-
regions might be related to the known elevation-dependent
warming (EDW), where high-mountain environments expe-
rience more rapid temperature changes than environments
at lower elevations (Pepin etal. 2015). Recent evidence
of EDW in the Andes of Northern and Central Chile has
been reported using observation and modeling approaches
70°W
17°S
29°S
40°S
47°S
56°S
SSP126
70°W
17°S
29°S
40°S
47°S
56°S
SSP245
70°W
17°S
29°S
40°S
47°S
56°S
SSP370
70°W
17°S
29°S
40°S
47°S
56°S
SSP585
−40 −30 −20 −10 0 10 20 30 40
Precipitation change (%)
Fig. 10 CMIP6 precipitation change (%) projections for the end of
the century under four future emission scenarios. Changes are com-
puted as the ensemble mean for 2080–2099 in relation to the refer-
ence period (1986–2014) for 27 models availables for all four sce-
narios. Black dots represent grid points where at least 90% of GCMs
agree on the sign of change. Latitudinal dashed lines show the
approximate limits of each subregion (Fig.1)
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2492 Á.Salazar et al.
1 3
(Aguilar-Lome etal. 2019; Bambach etal. 2022). Though
effective EDW is challenging to validate because sparse
high-elevation weather stations and high cloud cover hin-
der satellite analysis (Pabón-Caicedo etal. 2020), its con-
sequences may significantly impact cryospheric systems,
hydrological regimes, ecosystems, settlements, and produc-
tive systems.
We finally investigated the relationship between pro-
jected precipitation and temperature change for the end of
the century in the high-emission scenario SSP585. This is
plotted on a two-dimensional space in Fig.12 and reveals
the general pattern of projected changes and the behavior
of individual GCMs across different subregions. The trend
for sub-regionally averaged change in precipitation and
temperature is evident in Central Chile, where GCMs are
clustered towards the axis of negative precipitation and
positive temperature change ranging from 2.2 to − 37.6%
and 2.2 to 5.3°C, respectively. A clear pattern of change is
also visible in Northern Patagonia, where all models pro-
ject a negative shift in precipitation from − 6.6 to − 31.5%
and a positive temperature change from 1.69 to 4.42°C.
For the remaining subregions, the pattern of precipitation
change is not conclusive. However, all project positive
changes in temperature from 2.85 to 6.15°C in Northern
Chile and from 1.43 to 4.19°C in Southern Patagonia
(Fig.12).
Interestingly, the GCM that shows the greatest incre-
ment in temperature for Central Chile is CanESM5
(5.3°C), which ranked as the model with the high-
est TSS value (Table4). Recently, Rivera and Arnould
(2020) reported the same model as the best performing
in describing the current precipitation pattern for Central
Chile. However, given that CanESM5 is identified as a ‘hot
model’ (Scafetta 2022) its projections must be taken care-
fully unless model weighting or rescaling the ensemble is
applied to avoid highly biased projections (Tokarska etal.
2020). Also, choosing the ensemble with the more reliable
models has been proposed (Scafetta 2021). Similarly, the
‘hot model’ ACCESS-CM2 was the best performing model
for Northern Chile, and therefore the same care must be
applied in using its raw projections.
70°W
17°S
29°S
40°S
47°S
56°S
SSP126
70°W
17°S
29°S
40°S
47°S
56°S
SSP245
70°W
17°S
29°S
40°S
47°S
56°S
SSP370
70°W
17°S
29°S
40°S
47°S
56°S
SSP585
−6 −4 −2 0 2 4 6
Temperature change (°C)
Fig. 11 CMIP6 mean annual temperature change projections (°C) for
the end of the century under four future emission scenarios. Changes
are computed as the ensemble mean of 2080–2099 in relation to the
reference period (1986–2014) for 27 models available for all four sce-
narios. Black dots represent grid points where temperature changes
are greater than twice the standard deviation of the reference period
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2493CMIP6 precipitation andtemperature projections forChile
1 3
−20 −10 100 20 30
Precipitation Change (%)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
Temperature Change (°C)
1*
2
3
4
5
6
7*
8*
9
10
11*
12*
13
14
15
16
17
18
19
20
21
22
23
24
25*
26
27
28
29
30
31*
32
33
34
35
a) Northern Chile
−30 −20 −10 0
Precipitation Change (%)
2.5
3.0
3.5
4.0
4.5
5.0
Temperature Change (°C)
1*
2
3
4
5
6
7*
8*
9
10
11*
12*
13
14 15
16
17
18
19
20
21
22 23
24
25*
26
27
28
29
30
31*
32
33
34
35
b) Central Chile
−30 −25 −20 −15 −10
Precipitation Change (%)
2.0
2.5
3.0
3.5
4.0
Temperature Change (°C)
1*
2
3
4
5
6
7*
8*
9
10
11* 12*
13
14
15
16
17
18
19
20
21 22
23 24
25*
26
27
28
29
30
31*
32
33
34
35
c) Northern Patagonia
−15 −10 −5 0 5
Precipitation Change (%)
1.5
2.0
2.5
3.0
3.5
4.0
Temperature Change (°C)
1*
2
3
4
5
6
7*
8*
9
10
11*
12*
13
14
15 16
17
18
19
20
21 22
23
24
25*
26
27
28
29
30
31*
32
33
34
35
d) Southern Patagonia
1*. ACCESS-CM2
2. ACCESS-ESM1-5
3. AWI-CM-1-1-MR
4. BCC-CSM2-MR
5. CAMS-CSM1-0
6. CAS-ESM2-0
7*. CESM2-WACCM
8*. CIESM
9. CMCC-CM2-SR5
10. CMCC-ESM2
11*. CanESM5
12*. E3SM-1-1
13. EC-Earth3
14. EC-Earth3-CC
15. EC-Earth3-Veg
16. EC-Earth3-Veg-LR
17. FGOALS-f3-L
18. FGOALS-g3
19. FIO-ESM-2-0
20. GFDL-CM4
21. GFDL-ESM4
22. IITM-ESM
23. INM-CM4-8
24. INM-CM5-0
25*. IPSL-CM6A-LR
26. KACE-1-0-G
27. MIROC6
28. MPI-ESM1-2-HR
29. MPI-ESM1-2-LR
30. MRI-ESM2-0
31*. NESM3
32. NorESM2-LM
33. NorESM2-MM
34. TaiESM1
35. Ensemble
Fig. 12 Precipitation change (%) versus temperature change (°C) over
Chile for the end of the century (2080–2099) in relation to the ref-
erence period (1986–2014). Change values shown correspond to 34
GCMs available for emission scenario SSP585 spatially averaged
over each subregion. Circles represent the top 5 performed models
based on the TSS value. ‘Hot models’ are identified with an asterisk
symbol (*)
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2494 Á.Salazar et al.
1 3
4 Summary andconclusions
We have evaluated the capability of 36 GCMs from the
CMIP6 dataset to reproduce precipitation and temperature
against multiple observation datasets for four subregions
of Chile and analyzed their projections to the end of the
century (2080–2099) in relation to the historical period
(1986–2014) for four emission scenarios. A group of met-
rics was applied to test GCMs in reproducing the tempo-
ral and spatial pattern of precipitation and temperature.
A ranking was used to identify the models best aligned
with observations. Though models can replicate the mean
climate, they display varying variability in the spatiotem-
poral description of precipitation and temperature across
subregions. The bias identified in the extremely dry and
wet subregions, and the distinct climatic features that
characterize Chile’s climatic regimes, stress the need to
update the reference subregions of the Intergovernmental
Panel of Climate Change as proposed in our study. CMIP6
GCMs present warm and wet bias in Northern Chile and
Southern Patagonia. Whether this bias is related to the
scarcity of ground station data affecting the quality of
gridded observations or models’ structure and climate
variability remains uncertain. We do know these regions
have a sparse number of observations and we demonstrate
that gridded observations present substantial differences
for precipitation and so there is some level of uncertainty
in the validation process. Compared to these subregions,
Central Chile presents a dense observational network and
shows the lowest MBE and NRMSE values. Interestingly,
the best models identified based on TSS values for North-
ern (ACCESS-CM2) and Central Chile (CanESM5) were
models with high ECS, and therefore the corresponding
projections must be taken cautiously. Though using a large
model ensemble (36) may have limited this warm bias,
future work should use an ensemble of selected GCMs
with realistic values of ECS or a model weighting, among
other techniques.
According to CMIP6 models, Central Chile reports one
of the greatest future changes in precipitation and tempera-
ture and, therefore, represents a climate-change hotspot.
The ensemble mean projections for SSP585 scenarios indi-
cate a significantly drier (up to 30–40%) and warmer (up to
4–5°C) climate that call for an urgent need for the imple-
mentation of strong adaptative and mitigation measures
during the coming years. The decrease in precipitation
becomes robust (in terms of the model agreement on the
sign of the changes) from the relatively optimistic sce-
nario SSP245 and is substantial under the highest emission
scenario SSP585. Based on our findings, the application
of bias correction methods can help decrease the uncer-
tainty of precipitation projections for the Andean areas,
particularly in the extremely dry and wet subregions (Beck
etal. 2020; Peng etal. 2022). Yet, results might imply
an enhancement of precipitation and temperature changes
observed during the last decades (Garreaud etal. 2020)
and impose additional pressure on an area that supports
most of the country’s population and industrial produc-
tion. High-temperature increase in the Andes mountains
in all subregions might suggest a significant elevation-
dependent warming that can significantly impact Andean
snow and ice cover and thus water availability for human
consumption, hydropower generation, and the economy
(Cordero etal. 2019; Vicuña etal. 2021). The increase
in temperature in the studied subregions is expected to
accelerate the hydrological cycle and, in combination with
the projected precipitation change, can increase the fre-
quency and severity of hydrological and climatological
droughts, heat waves, and glacier mass loss acceleration
(Ayala etal. 2020; Dussaillant etal. 2019; Pellicciotti etal.
2014). The projected warming can also have severe con-
sequences over the frequency and extension of wildfires
(González etal. 2018; Urrutia-Jalabert etal. 2018), affect
tree growth decline in remaining natural forests (Matsko-
vsky etal. 2021), change plant community composition
in Andean ecosystems by modifying the tree-line frontier
in Southern Patagonia (Aguirre etal. 2021) and increase
the advent of invasive species in a variety of ecosystems
(Schroeder etal. 2023).
As socio-ecological systems’ adaptation capacity to a
changing climate remains one of the main challenges of the
coming decades, we hope that the results presented in this
study will help to understand forthcoming climate risks bet-
ter, support decision-making processes, and further research.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00382- 023- 07034-9.
Author contributions All authors contributed to the study conception
and design. Material preparation, data collection and analysis were
performed by ÁS and MT. The first draft of the manuscript was writ-
ten by ÁS and KG and all authors commented on previous versions of
the manuscript.
Funding This work was supported by FONDECYT postdoctoral Grant
3190563 and Grant ANID/BASAL FB210006. We also acknowl-
edge the support from FONDECYT 1201742 Grant, Concurso de
Fortalecimiento al Desarrollo Científico de Centros Regionales
2020-R20F0008-CEAZA and ANID ACT210046 grant. We thank the
climate modeling groups involved in CMIP6 for producing and making
available their simulations.
Data availability Data from the Coupled Model Intercomparison
Project, Phase 6 (CMIP6) is available at https:// esgf- node. llnl. gov/
proje cts/ cmip6/. Gridded precipitation data from observations can be
accessed at https:// www. chc. ucsb. edu/ data/ chirps (CHIRPS), https://
psl. noaa. gov/ data/ gridd ed/ data. cmap. html (CMAP), https:// www. cr2.
cl/ datos- produ ctos- grill ados/ (CR2), https:// cruda ta. uea. ac. uk/ cru/ data/
hrg/ cru_ ts_4. 05/ (CRU), https:// disc. gsfc. nasa. gov/ datas ets/ GPCPM
ON_3. 2/ summa ry (GPCP), https:// clima tedat aguide. ucar. edu/ clima
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2495CMIP6 precipitation andtemperature projections forChile
1 3
te- data/ persi ann- cdr- preci pitat ion- estim ation- remot ely- sensed- infor
mation- using- artifi cial (PERSIANN), http:// resea rch. jisao. washi ngton.
edu/ data_ sets/ ud/ (University of Delaware). Gridded temperature data
from observations can be accessed at https:// www. cr2. cl/ datos- produ
ctos- grill ados/ (CR2), https:// cruda ta. uea. ac. uk/ cru/ data/ hrg/ cru_ ts_4.
05/ (CRU) and http:// resea rch. jisao. washi ngt on. edu/ data_ sets/ ud/ (Uni-
versity of Delaware).
Declarations
Conflict of interest The authors have no relevant financial or non-fi-
nancial interests to disclose.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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