Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

Abstract

Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

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SCIENTIFIC DATA | (2020) 7:338 | https://doi.org/10.1038/s41597-020-00681-1
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Bias-corrected climate projections
for South Asia from Coupled Model
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Vimal Mishra
 ✉ Bhatia
 & Amar Deep Tiwari
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Background & Summary
South Asia is one of the most densely populated regions of the world. A majority of the population in South Asia
depends on agriculture for their livelihood. South Asia is among the global hot spots that are likely to face the
detrimental impacts of climate change1,2. Considerable changes in precipitation and temperature are projected
in South Asia that will have implications for water resources and agriculture3–6. e risk of oods and droughts
are likely to increase in South Asia under the warming climate7–12. Both recent droughts and oods have aected
a large population and caused enormous damage to agriculture and infrastructure in South Asia13–16. Similarly,
the frequency and intensity of severe heatwaves have increased in South Asia and projected to increase in the
future17–21. Overall, the frequency of both precipitation and temperature extremes has considerably increased in
the past decades and likely to rise further under the warming climate18,22.
Projections from the General Circulation Models (GCMs) play a vital role in understanding the future changes
in climate. However, spatial resolution at which GCMs are run is oen too coarse to get reliable projections at the
regional and local scale23. Precipitation and temperature projections at higher spatial resolution are required for
the climate impact assessments24–26. Moreover, precipitation and temperature from the GCMs have a bias due to
their coarse resolution or model parameterizations27,28. erefore, for the assessment of the climate change and
its impacts on dierent sectors (e.g., water resources, agriculture), bias-correction is required23,29–34. Both statis-
tical and dynamical approaches are used for downscaling and bias correction of climate change projections from
GCMs. Statistical approaches are based on the distribution and relationship between the observed and projected
data for the historical period33,34. On the other hand, dynamical downscaling approaches are based on regional
climate model forced with the boundary conditions from the coarse resolution GCMs35,36. Both statistical and
dynamical downscaling approaches have limitations37,38. e primary limitation of the dynamical downscaling
is related to the requirement of computational eorts to run the regional climate models at higher spatial and
temporal resolution27,39. Moreover, dynamical downscaling may not remove the bias in climate variables, which
might require corrections based on the statistical approaches39. Given these limitations, statistical bias correction
approaches are widely used in climate change impact assessments40,41.
Considering the climate change impacts in South Asia, we develop a bias-corrected dataset of daily precipita-
tion, maximum and minimum temperatures using output from 13 GCMs that participated in the Coupled Model
1Civil Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat, 382355, India. 2Earth Sciences,
Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat, 382355, India. ✉e-mail: vmishra@iitgn.ac.in


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Intercomparison Project-6 (CMIP6). e 13 GCMs were selected based on the availability of daily precipitation,
maximum and minimum temperatures for the historical and four scenarios (SSP126, SSP245, SSP370, SSP585).
We used empirical quantile mapping (EQM) to develop bias-corrected data at daily temporal and 0.25° spatial
resolution for six countries in South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka). Also,
the bias-corrected projections are developed for 18 sub-continental river basins. e bias-corrected projections
from 13 CMIP-GCMs can be used for estimating the projected changes in mean and extreme climate in South
Asia. Bias corrected data for 18 sub-continental river basins can be used to develop hydrologic projections using
hydrological models.
Methods
Bias-corrected projections were developed for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and
Sri Lanka) and the 18 Indian sub-continental river basins (Fig.1). We used basin boundaries in the Indian
sub-continent from Shah and Mishra42 [Fig.1]. We obtained observed daily gridded precipitation, minimum
and maximum temperatures for South Asia for the 1951–2018 period. Daily precipitation at 0.25° was obtained
from the India Meteorological Department (IMD) for the Indian region43. Pai et al.43 developed gridded daily
precipitation for India using station observations from more than 6000 stations located across India. e precip-
itation captures critical features of the Indian summer monsoon, including higher rainfall in the Western Ghats
and northeastern India and lower rainfall in the semi-arid and arid regions of western India. Besides, gridded
precipitation captures the orographic rain in the Western Ghats and foothills of Himalaya. e gridded precipi-
tation data from IMD has been used for various hydroclimatic applications13,44,45. Gridded daily maximum and
minimum temperatures from IMD were developed using station-based observations from more than 350 stations
located across India46. ere is bias in temperature observations from IMD in the Himalayan region, which can
be attributed to sparse station density44,47. Gridded precipitation and maximum and minimum temperatures were
obtained from Sheeld et al.48 for the regions outside India. Datasets from Sheeld et al.48 are available at 0.25°
spatial and daily temporal resolutions. Consistency between IMD and Sheeld et al.48 dataset was checked in
Shah and Mishra42, who reported that Sheeld et al.48 dataset has a good agreement with the IMD observations.
Nonetheless, we used IMD gridded dataset for the Indian region and Sheeld et al.48 for outside India for bias
correction of projections from CMIP6 as the IMD data is widely used for hydroclimatic studies in India. We used
gridded observations for bias correction as station data are not available.
We obtained daily precipitation, maximum and minimum temperatures from 13 CMIP6-GCMs from https://
esgf-node.llnl.gov/search/cmip6/. All the three variables and for all the scenarios were available only for these 13
GCMs. erefore, we restricted bias-correction to only these models. Precipitation, maximum and minimum
temperatures from CMIP6-GCMs are available at dierent spatial resolutions (TableS1). For instance, the spatial
resolution of the CMIP6 projection varies from 0.7° (EC-Earth3) to more than 2° (CanESM5). All the three varia-
bles were selected for the historical (1850–2014), ssp126 (2015–2100), ssp245 (2015–2100), ssp370 (2015–2100),
and ssp585 (2015–2100) scenarios under r1i1p1f1 initial condition at daily time scale49. e scenarios used in
the CMIP6 combine Shared Socioeconomic Pathways (SSP) and target radiative forcing levels at the end of the
21st century50. For instance, SSP126 indicates SSP-1 and target radiative forcing at the end of the 21st century 2.6
Watt/m2. erefore, SSP126 is a mitigation scenario. On the other hand, SSP585 is based on the emission scenario
considering SSP-5 and radiative forcing of 8.5 Watt/m2 at the end of the 21st century50. Further details on the
scenarios used in the CMIP6 can be obtained from Gidden et al.50. We regridded all the variables from CMIP6 to
Fig. 1 Geographical domains for bias-corrected CMIP6 projections. (a) Indian Subcontinent river basin
boundaries (black) with the streamlines (blue). Topography in the color scale is shown in the background.
Names of the sub-continental river basins are written within the basin boundaries. (b) South Asian country
boundaries (black) where topography in the color scale is shown in the background.
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1° spatial resolution to make them consistent. However, the eect of regridding using bilinear interpolation was
checked by comparing the gridded datasets against the raw data for all-India mean of precipitation, maximum
and minimum temperatures. We did not nd any considerable dierences in the all-India averaged precipitation
and temperature using regridded and raw output from the GCMs (Fig.S1).
Outputs of the various atmospheric (e.g., maximum and minimum temperatures, and precipitation) vari-
ables obtained from GCMs are known to exhibit systematic biases (Fig.S2). Hence, these outputs need to be
bias-corrected to produce reliable estimates at regional and local scales for climate impact assessment. To achieve
this, statistical transformations that attempt to nd a function that maps the model output to a new distribution
such that the resulting distribution matches that of observations. In general, this transformation can be formu-
lated as Piani et al.30:
=xf(x )(1)
m
om
where
xm
o
is the bias-corrected model output. If the statistical distribution of
xm
and
x0
are known, the transfor-
mation can be written as:
=
−
xF(F (x )) (2)
m
o01mm
where
Fm
and
Fo
are the Cumulative Distribution Functions (CDFs) of
xm
and
xo
respectively.
In Empirical Quantile Mapping (EQM44,51), instead of assuming parametric distributions, empirical
CDFs34,52,53 are estimated from the percentiles calculated from
xm
and
x0
. As a result, EQM and its variants can be
applied to both temperature and precipitation even if their underlying distributions are dierent and hence rec-
ommended for statistical bias correction54.
In the context of statistical downscaling, since the observations are at a higher resolution than models, EQM
on bilinearly interpolated model outputs at observation resolution is oen used to address the scale mismatch
and generate post-processed model outputs44. We choose non-parametric transformation approaches over the
parametric approaches as has shown better skills in the comparison to parametric methods in reducing biases
from GCM as well as Regional Climate Model (RCM) outputs55.
Fig. 2 Projections of precipitation, maximum and minimum temperatures for the end of 21st century using raw
output from CMIP6-GCMs. (a–d) Multimodel ensemble mean projected change in mean annual precipitation
(%) for the Far (2074–2100) with respect to the historical period (1988–2014), (e–h) same as (a–d) but for
the mean annual maximum temperature, (i–l) same as (a–d) but for the mean annual minimum temperature.
Median of the multimodel ensemble mean precipitation; maximum and minimum temperatures are shown
in each panel. Projected changes were estimated for the four scenarios (SSP126, SSP245, SSP370, and SSP585)
against the historical period.
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We used EQM to statistically downscale the daily maximum and minimum temperatures, and precipitation
for South Asia and Indian sub-continental river basins (Fig.1). We use the outputs (xm) from 13 CMIP6-GCMs
(TableS1), which are available at dierent resolutions (TableS1). Observations for the three variables at the reso-
lution of 0.25-degree are obtained from the IMD, Pai et al.)43 for Indian Region and Sheeld et al.48 for grid-points
within and outside India, respectively. We used the 1951–2014 period to obtain the transformation function to
map the distribution of xm to xo. For precipitation, the drizzle eect is corrected by using a wet day threshold of
1 mm/day30,55. If the values from model projections are larger (smaller) than the training values used to estimate
the empirical CDF, the correction found for the highest (lowest) quantile of the training period is used. We used
mapped transformation to bias correct the outputs for the historical period and the SSP126, SSP245, SSP370, and
SSP585 scenarios for the 2015–2100 period for all the three variables. Raw and bias-corrected data for INM-CM5
is shown against the observed maximum temperature for a randomly selected grid in the Indian subcontinent
(Fig.S1). Quantile mapping based statistical bias correction has been widely used, and its performance was found
to be satisfactory in comparison to the other methods25,34,56.
Fig. 3 Multimodel ensemble mean bias in precipitation, maximum and minimum temperatures in 13 CMIP6-
GCMs. (a) Bias (%) in mean annual precipitation for the historical period (1985–2014), (b) bias in mean annual
precipitation (%) aer the bias correction, (c,d) Bias (°C) in mean annual maximum temperature before and
aer bias correction, and (e,f) bias (°C) in mean annual minimum temperature before and aer bias correction.
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
Bias corrected daily precipitation, maximum and minimum temperatures are available for the 13 GCMs
(TableS1) for the historical (1951–2014) and future (2015–2100) periods. We also provide the reference gridded
observed data of daily precipitation, maximum and minimum temperatures that were used for the bis correc-
tion. Projections for the future are available for the four scenarios (SSP126, SSP245, SSP370, and SSP585) for
South India (India, Pakistan, Bangladesh, Sri Lanka, Bhutan, and Nepal) and Indian sub-continental river basins
(Fig.1). e basin wise dataset57 and country wise dataset58 have been made available through Zenodo. Details on
the data format can be obtained from a readme le provided at the above link.
Technical Validation
First, we estimated the projected changes in mean annual precipitation, maximum and minimum temperatures
using the raw data from the CMIP6-GCMs (Fig.2). e projected changes were estimated for each GCM for the
late 21st century (2074–2100) against the historical reference period (1988–2014). en, the multimodel ensemble
mean of the projected changes from all the 13 CMIP6-GCMs was taken. e multimodel ensemble mean annual
Fig. 4 Multimodel ensemble mean bias in the 90th percentile of precipitation, maximum and minimum temperatures
in 13 CMIP6-GCMs. (a) Bias (%) in extreme precipitation for the historical period (1985–2014), (b) bias in extreme
precipitation (%) aer the bias correction, (c,d) Bias (°C) in extreme maximum temperature before and aer bias
correction, and (e,f) bias (°C) in extreme minimum temperature before and aer bias correction. e 90th percentile
of daily precipitation was estimated using rainy days with precipitation more than 1 mm.
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precipitation is projected to increase in South Asia under the projected future climate (Fig.2a–d). e projected
increase in precipitation in South Asia under the future climate varies with the scenario considered. For instance,
under the high-emission scenario (SSP585), a considerably higher increase (more than 30%) in the multimodel
ensemble mean is projected in comparison to the low emission (SSP126) scenario (less than 13%). Similarly, there
are regional dierences in the projections of precipitation from the CMIP6-GCMs. For example, a more substan-
tial increase in the multimodel ensemble mean precipitation is projected for the semi-arid and arid regions of
western South Asia than the other regions (Fig.1a–d). Similar to rainfall, mean annual maximum and minimum
temperatures are projected to rise substantially in South Asia under the future climate (Fig.2). Projected changes
in mean annual minimum temperature are generally greater than the changes in mean annual maximum temper-
ature. As expected, the high emission scenario (SSP585) will lead to a much higher rise in temperatures than the
low emission scenario of SSP126 (Fig.2).
e raw datasets of precipitation, maximum and minimum temperatures can be used to estimate the projected
changes in South Asia under the future climate for dierent scenarios. However, climate impact studies need
bias-corrected projections for decision making at regional and local scales. Since the bias-corrected dataset is
consistent with observation for a climatological mean period, it is easier to infer the project changes and its impli-
cations in dierent sectors (e.g., water resources and agriculture) for observations. We, therefore, bias-corrected
precipitation, maximum and minimum temperatures for the historical (1951–2014) and future (2015–2100) peri-
ods for all the four scenarios for South Asia and Indian sub-continental river basins. e bias-corrected dataset
can be used for any region or river basin in South Asia or the Indian sub-continent (Fig.1).
We estimated the multimodel ensemble mean bias in precipitation, maximum and minimum temperatures
from the 13 CMIP6-GCMs (Fig.3). e bias in mean annual precipitation, maximum and minimum tempera-
tures was estimated against the observations from IMD (for the Indian domain) and Sheeld et al.48 observations
(for outside India). e CMIP6-GCMs show a dry bias (15–20%) in mean annual precipitation in the majority of
South Asia (Fig.1a). On the other hand, the multimodel ensemble mean positive bias in mean annual precipita-
tion was found in the regions located in Nepal, Pakistan, and Peninsular India (Fig.3a). A high cold bias in both
mean annual maximum and minimum temperatures were found in the Himalayan region in the CMIP6-GCMs
Fig. 5 Seasonal cycle of bias-corrected precipitation, maximum and minimum temperatures. Comparison of
the multimodel model ensemble (blue) mean seasonal cycle of bias-corrected (a) precipitation, (b) maximum
temperature, and (c) minimum temperature against the observations for the 1985–2014 period (red). e
shaded area represents uncertainty (one standard deviation) of all 13 CMIP6-GCMs.
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(Fig.3c,e). Also, CMIP6-GCMs exhibit warm bias in mean annual minimum temperature in the majority of
South Asia except for the Himalayan region (Fig.3e). We applied the EQM approach to correct the bias in the
CMIP6-GCM output at daily timescale. e bias was substantially reduced aer the bias correction in all the three
variables for the historical (1985–2014) period (Fig.3b–f). e reduction in bias in mean annual precipitation,
maximum and minimum temperatures shows the eectiveness of our bias correction approach based on EQM.
Similar to mean annual precipitation, maximum and minimum temperatures, we estimated bias in precip-
itation and temperatures extremes in the raw output from the CMIP6-GCMs (Fig.4). e 90th percentiles of
precipitation of rainy days (precipitation more than 1 mm), maximum and minimum temperatures were com-
pared for the historical period (1985–2014) from CMIP6-GCMs against the observed dataset. Consistent with
mean annual precipitation, a considerable dry bias is present in extreme precipitation in CMIP6-GCMs across
South Asia (Fig.4a). We nd that the CMIP6-GCMs show a warm bias in the 90th percentile of maximum and
minimum temperatures across South Asia except in the Himalayan region (Fig.4c,e). In the Himalayan region,
a cool bias in CMIP6-GCMs in maximum and minimum temperature extreme was found (Fig.4c,e). We nd
that the EQM based bias correction has successfully removed the bias in extreme precipitation, maximum and
minimum temperatures across South Asia (Fig.4). erefore, the bias in both mean and extremes of precipita-
tion, maximum and minimum temperatures were removed. Also, we compared the season cycle of bias-corrected
precipitation, maximum and minimum temperatures from the CMIP6-GCMs against the observed dataset for
the 1985–2014 period. Uncertainty in the bias-corrected precipitation, maximum and minimum temperatures
were estimated using one standard deviation. We nd that the seasonal cycle of the multimodel ensemble mean
bias-corrected precipitation, maximum, and minimum temperatures compare well against the observations
(Fig.5). Moreover, the covariability of the monsoon season precipitation and air temperature is well captured by
the bias-corrected dataset (Fig.S3). Overall, our results show that the EQM approach successfully corrects the
bias in the CMIP6-GCMs, which can be used for climate impacts studies in South Asia. Also, the bias-corrected
dataset can be used for hydrological studies in the Indian sub-continental river basins.
 Daily bias-corrected
projections of precipitation, maximum and minimum temperatures at 0.25° from CMIP6-GCMs are devel-
oped for South Asia and the 18 Indian sub-continental river basins (Fig.1). e projections are available for
Fig. 6 Projections of precipitation, maximum and minimum temperatures for the end of the 21st century using
bias-corrected data from CMIP6-GCMs. (a–d) e multimodel ensemble mean projected change in mean
annual precipitation (%) for the Far (2074–2100) with respect to the historical period (1988–2014), (e–h) same
as (a–d) but for the mean annual maximum temperature, (i–l) same as (a–d) but for the mean annual minimum
temperature. Median of the multimodel ensemble mean precipitation; maximum and minimum temperatures
are shown in each panel. Projected changes were estimated for the four scenarios (SSP126, SSP245, SSP370, and
SSP585) against the historical period.
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the historical (1951–2014) and future (2015–2100) periods. We estimated projected changes in precipitation,
maximum and minimum temperatures in the late 21st century from the bias-corrected dataset against the
historical reference (1988–2014) period for all the scenarios (SSP126, SSP245, SSP370, and SSP585) [Fig.6].
Our bias-corrected precipitation projections show consistent spatial patterns that were observed in the raw
CMIP6-GCMs (Fig.2). For instance, a larger increase in mean annual precipitation was found in the western
parts of South Asia in both raw and bias-corrected datasets (Fig.6a–d). A considerably large increase in mean
annual precipitation is projected under SSP585 (median 23%) than under SSP126 (median 12%) [Fig.6a–d]. e
ensemble mean median change in maximum temperature is projected to be 1.3 °C in SSP126 and 2.2 °C in SSP585
(Fig.6e–h). Similarly, the ensemble mean minimum temperature is projected to rise signicantly across South
Asia with a median increase of more than 3 °C increase in SSP585 scenario (Fig.6i–l).
Projected changes in precipitation, maximum and minimum temperatures were estimated using a 30-year
moving window for all the six countries in South Asia under the highest emission scenario of SSP585 (Fig.7).
We considered the SSP585 scenario to estimate the projected change in precipitation, maximum and minimum
−10
0
10
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Bangladesh
Precipitation (%)
(a)
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(b)
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(c)
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2015 2025 2035 2045 2055 2065 2075 2085 2095
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(q)
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2015 2025 2035 2045 2055 2065 2075 2085 2095
(r)
Fig. 7 Multimodel ensemble mean change in precipitation, maximum and minimum temperatures in South
Asia. Countrywise changes in the multimodel ensemble mean annual precipitation (%), maximum temperature
(°C), and minimum temperature (°C) estimated using a 30-year moving window against the historical reference
period of 1985–2014. e shaded region shows uncertainty (estimated using one standard deviation) based on
13 CMIP6-GCMs.
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temperatures under the worst case (Fig.7). Projected change in each CMIP6-GCM was estimated for each
30-year window (1986–2015, 1987–2016 … 2071–2100) against the historical reference period of 1985–2014.
Moreover, we estimated uncertainty in the bias-corrected CMIP6-GCMs using one standard deviation of pro-
jected change in the individual GCMs. e multimodel ensemble mean annual precipitation is projected to rise
in all the six countries under the future climate (Fig.7). All the six countries in South Asia are projected to
experience a 20–40% rise in mean annual precipitation under the SSP585 scenario by the end of the 21st century.
However, the bias-corrected precipitation projections show more uncertainty for Pakistan than the other coun-
tries (Fig.7). Uncertainty in the bias-corrected maximum and minimum temperatures is substantially lesser than
that of precipitation (Fig.7). e multimodel ensemble mean bias-corrected mean annual maximum temperature
is projected to rise by 3–4 °C by the end of the 21st century under SSP585. Moreover, the bias-corrected ensemble
mean annual minimum temperature is projected to rise by 3–5 °C by the end of the 21st century (Fig.7). We nd
a dierent level of uncertainty in mean annual precipitation, maximum and minimum temperatures for the six
countries in South Asia (Fig.7). Overall, the climate is projected to become wetter and warmer in South Asia in
the future, and the magnitude of change will depend on the scenarios.
We estimated projected changes in mean annual precipitation, maximum and minimum temperatures for the
six countries, and 18 sub-continental river basins for the Near (2020–2046), Mid (2047–2073), and Far (2074–
2100) periods against the historical reference of 1988–2014 (TablesS2–S7). e multimodel projected changes
were estimated for all the four scenarios along with the mean for the historical period (TableS2-S7). e multi-
model ensemble mean bias-corrected precipitation is projected to change between 3–20% in the Near term under
the SSP126 (TableS2). e most substantial increase in precipitation is projected in Pakistan, while the lowest rise
Fig. 8 Changes in the frequency of extreme precipitation, maximum and minimum temperature in the state
of Uttar Pradesh. Projected changes in the frequency of precipitation (a–c), maximum temperature (d–f), and
minimum temperature (g–i) extremes estimated using 95the percentile of rainy days (precipitation more than
1 mm) and 95th percentile of maximum and minimum summer (April-May) temperatures for the state of Uttar
Pradesh (India). Median frequency is shown in each panel. Changes in the frequency were estimated against the
historical reference period of 1988–2014.
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is expected in Bhutan. Precipitation is projected to rise substantially in the Far term in all the countries in South
Asia under the SSP585 (TableS2). e ensemble mean bias-corrected precipitation is projected to change by
31–53%, with the most considerable projected rise in Pakistan in the Far term under SSP585 (TableS2). e pro-
jected increase in mean annual maximum temperature is far lesser (0.48–0.97 °C) in the Near term under SSP126
in comparison to the Far (2.6–5.3 °C) term under the SSP585 (TableS3) in the six countries in South Asia. Our
bias-corrected data based on 13 CMIP6-GCMs projected an increase of 0.7–1.3 °C) in mean annual minimum
temperature in the Near term under SSP126 (TableS4).
Moreover, mean annual minimum temperature is projected to rise between 3.5 to 5.5 °C in the late 21st cen-
tury under the SSP585 (TableS4). Uncertainty in the projections of bias-corrected precipitation, maximum and
minimum temperatures was estimated for each country and period under all the four scenarios (TablesS2–S4).
Temperature projections show lesser uncertainty than the projections of precipitation, which might have impli-
cations for hydrologic applications of the bias-corrected projections (TablesS2–S4).
We estimated projected changes in mean annual precipitation, maximum and minimum temperatures using
bias-corrected data from the 13 CMIP6-GCMs for the 18 sub-continental river basins (TablesS5–S7, Fig.1). Bias
corrected projections of the three climatic variables are essential for hydrologic modelling, and the climate change
impact assessment. Mean annual precipitation is projected to rise across the basins under all the scenarios in the
projected future climate (TableS5). e projected rise in the mean annual precipitation is considerably higher
in the SSP585 in comparison to the SSP126. e projected rise in precipitation in the sub-continental basins is
higher in the Far period than the Near period.
Notwithstanding a considerable uncertainty in the precipitation projections, bias-corrected data show that
precipitation is projected to rise more in the river basins located in the semi-arid/arid regions of the Indian
sub-continent (TableS5). Similarly, signicant warming in the mean annual maximum and minimum temper-
atures is projected based on the bias-corrected data from the 13 CMIP6-GCMs (TablesS6, S7). Mean annual
maximum temperature is projected to rise between 2.5–4.4 °C in the Far period under SSP585 in the Indian
sub-continental river basins (TableS6). Moreover, the mean annual minimum temperature is projected to rise by
3.0–5.0 °C in the Far period under SSP585 in the river basins of the Indian sub-continent (TableS7). Basin specic
projections and associated uncertainty can be seen in supplemental TablesS5–S7. Overall, the bias-corrected
projections can be used for the hydroclimatic impact assessment in the sub-continental river basins.
Fig. 9 Changes in the frequency of extreme precipitation, maximum and minimum temperature in the state of
Godavari basin. Projected changes in the frequency of precipitation (a–c), maximum temperature (d–f), and
minimum temperature (g–i) extremes estimated using 95th percentile of rainy days (precipitation more than
1 mm) and 95th percentile of maximum and minimum summer (April-May) temperatures for the state of Uttar
Pradesh (India). Median frequency is shown in each panel. Changes in the frequency were estimated against the
historical reference period of 1988–2014.
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
Daily bias-corrected projections of precipitation, maximum and minimum temperatures at 0.25° are essential for
climate impact assessment for the administrative boundaries or at river basin scale25. We developed bias-corrected
projections from 13 CMIP6-GCMs that can be used for hydroclimatic impact assessment based on mean and
extremes in South Asia. Bias corrected data performs well against the mean and extremes. e dataset has been
arranged based on the geographical boundaries of six countries in South Asia. Moreover, we provide separate data
for each of the 18 sub-continental river basins. Daily bias-corrected projections can be used to estimate climatic
indices associated to mean and extremes. For instance, daily maximum and minimum temperatures can be used
to estimated projected changes under dierent scenarios for the crop growing seasons. Moreover, the temperature
dataset can be used to estimate growing degree days (GDD)59 and other indicators of extreme heat during the
crop growing period60.
Similarly, daily precipitation projections can be used to estimate changes in mean and extreme precipitation
for any period during the 21st century22,27. Data users can also estimate the dierences in indicators and poten-
tial impacts based on the low (SSP126) and high (SSP585) emission scenarios. Most of the hydrological models
require daily precipitation, maximum and minimum temperatures as the primary inputs of meteorological forc-
ing. erefore, hydrological models can be used with the bias-corrected projections to estimate the impacts of the
projected future climate on hydrology for a river basin or a region.
As an example, we use the bias-corrected projections to estimate the frequency of precipitation and temper-
ature extremes for an administrative region (state of Uttar Pradesh, India) and a river basin (Godavari, India)
[Figs.8, 9]. e frequency of extreme precipitation was estimated using 95th percentiles of rainy days (precip-
itation more than 1 mm). Similarly, the frequency of extreme hot maximum and minimum temperatures was
estimated using the 95th percentile of the two hottest months (April-May) in the region. As expected, both precip-
itation and temperature extremes are projected to rise in Uttar Pradesh and Godavari basin under the SSP585 sce-
nario (Figs.8, 9). e projected rise in the frequency of precipitation and temperature extremes is higher for the
Far period than the Near-term climate. Overall, daily bias-corrected CMIP6 projections can be used for multiple
assessments related to climate and hydrology in one of the most populated regions of the world. As we provided
the bias-corrected data for individual GCMs, users can select the GCMs that perform well in the region of inter-
est. Moreover, the range of future projections can be estimated using the bias-corrected projections from the indi-
vidual GCMs (Fig.S4). In the future, bias-corrected projections will be made available from more CMIP6-GCMs
as their output becomes available. More details on the data can be found in the link and from the readme le.

Codes used for bias correction of CMIP6-GCMs are available through the Github link: https://github.com/
udit1408/cmip6_downscaling
Received: 11 June 2020; Accepted: 1 September 2020;
Published: xx xx xxxx

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Acknowledgements
We acknowledge the data availability from India Meteorology Department (IMD) and Sheeld et al. (2006).
Output from the CMIP6 models from https://esgf-node.llnl.gov/projects/cmip6/ is greatly acknowledged.is
work is supported by Ministry of Water Resources under National Water Mission.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

13
SCIENTIFIC DATA | (2020) 7:338 | https://doi.org/10.1038/s41597-020-00681-1
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V.M. designed the study and wrote the manuscript. A.T. downloaded and processed CMIP6 projections. U.B. did
the bias correction of CMIP6 projections.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41597-020-00681-1.
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