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Article https://doi.org/10.1038/s41467-024-53861-7
Using human observations with instrument-
based metrics to understand changing
rainfall patterns
V. Savo
1,2,3
,K.E.Kohfeld
2,4
,J.Sillmann
5,6
,C.Morton
2
,J.Bailey
2
,
A. S. Haslerud
5
,C.LeQuéré
7
&D.Lepofsky
1,8
Shifting precipitation regimes are a well-documented and pervasive con-
sequence of climate change. Subsistence-oriented communities worldwide
can identify changes in rainfall patternsthatmostaffecttheirlives.Herewe
scrutinize the importance of human-based rainfall observations (collated
through a literature review spanning from 1994 to 2013) as climate metrics and
the relevance of instrument-based precipitation indices to subsistence activ-
ities. For comparable time periods (1955-2005), changes observed by humans
match well with instrumental records at same locations for well-established
indices of rainfall (72% match), drought (76%), and extreme rainfall (81%),
demonstrating that we can bring together human and instrumental observa-
tions. Many communities (1114 out of 1827) further identify increased varia-
bility and unpredictability in the start, end, and continuity of rainy seasons, all
of which disrupt the cropping calendar, particularly in the Tropics. These
changes in rainfall patterns and predictability are not fully captured by existing
indices, and their social-ecological impacts are still understudied.
Shifting precipitation regimes are a well-documented consequence of
climate change, known to affect crop production, food security, the
incidence of water-borne diseases and human health1–6.However,itis
difficult to assess how regime shifts vary across regions7,8since pre-
cipitation patterns and volumes both differ and are changing differ-
ently across the globe3. In several regions around the world,
precipitation patterns simulated by climate models also remain
uncertain, particularly at the local level3,9. The largest uncertainties
among model projections occur in regions where a shift in the sign of
change in precipitation volumes is expected3,10,11. These uncertainties
contribute to precluding the interpretation of the ongoing and future
local climatic changes and their resulting impacts to local
communities.
Using climatic data to assess the social-ecological impacts of
climate change requires an understanding of how rainfall patterns
affect natural and agro-ecosystems as well as the human commu-
nities that depend on these ecosystems for their subsistence12.Sea-
sonal rainfall cycles set the timing over which plants synchronize
their phenology, animals adjust their mating seasons and farmers
decide their cropping calendar. Any future change to seasonality and
the associated intensity and volume of rainfall will impact species
distributions, crop production and human societies (e.g., refs. 13–16).
Thus, one challenge for projecting future climate change involves
developing indices that capture characteristics of precipitation that
are most important to human societies, especially those that are
dependent on reliable characteristics of rainfall for maintaining their
Received: 10 May 2018
Accepted: 24 October 2024
Check for updates
1
Hakai Institute, Heriot Bay, BC, Canada.
2
School of Resource and Environmental Management, Simon Fraser University, 8888 University Drive, Burnaby, BC,
Canada.
3
Department of Education Science, University Roma Tre, Rome, RM, Italy.
4
School of Environmental Science, Simon Fraser University, Burnaby, BC,
Canada.
5
Center for International Climate Research –Oslo, CICERO, Pb. 1129 Blindern, Oslo, Norway.
6
University of Hamburg, Research Unitfor Sustainability
and Climate Risks, Hamburg, Germany.
7
Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich
Research Park, Norwich, UK.
8
Department of Archaeology, Simon Fraser University, Burnaby, BC, Canada. e-mail: valentina.savo@uniroma3.it
Nature Communications | (2024) 15:9563 1
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food provisioning as are thousands of subsistence-oriented com-
munities worldwide.
Subsistence-oriented communities include indigenous and non-
indigenous people whose livelihood and culture largely depend on,
and are interlinked with, the local environment4. Because of this
interconnectedness, these communities have been shown to have a
deep understanding of broad environmental processes and functions
and their changes, based on the accumulation of experiences,adaptive
learning and intergenerational sharing of observations4. In the current
race against climate change, many researchers have recognised the
importance of the ecological knowledge of these communities to
better understand how the changing climate is impacting local envir-
onments and peoples17,18. In this paper, we combine this knowledge
with climate data, not to validate it19 but to increase our overall
understanding of local climatic changes and to identify which of those
changes are more relevant for local communities.
Here, we explore how to bridge the gap between information
conveyed by instrumental records and the information needed by
communities trying to adapt toclimatechange.Wecompare
observations by subsistence-oriented communities4with several
indices of precipitation characteristics from instrumental obser-
vations. Between 2012 and 2013, we assembled these human
observations of changes in precipitation and drought by con-
ducting a global meta-analysis4spanning sources from 1994 to
2013 and including human recollections up to 40 years prior to
the reporting dates. We performed the meta-analysis using peer-
reviewed and grey literature that reported observations of cli-
matic changes made by subsistence-oriented communities. We
then georeferenced these observations and compared them to
spatial patterns of climatic indices derived from instrument-
based data. These indices include (1) Total Precipitation20
(PRCPTOT shortened here for simplicity to TP); (2) extreme
rainfall20 (R95p); (3) drought as defined by Consecutive Dry
Days20 (CDD) and (4) drought as defined by the Palmer Drought
Severity Index21 (PDSI) (the detailed explanation of these indices
is reported in the cited references while technical details on the
databases can be found in Supplementary Table 1). We also
visually compared human observations with changes in the
Dimensionless Seasonality Index (DSI)2,22, which measures the
seasonal distribution of rainfall (changes in DSI can indicate
either a variation in annual rainfall or seasonal differences in
rainfall amounts). All these indices are estimated from global
instrument-based records for the period of 1955–2005, which we
deem to be roughly comparable to the time period covered by
the human observations4.Bycomparinginstrument-based
metrics and human observations at every location, we identify
regions of agreement and disagreement between the two types of
data. We then discuss what we can learn from this comparison
and explore how local observations can complement instrumental
indices. Finally, we discuss what kind of precipitation information
(i.e., new climatic measures) could help local communities be
better prepared to plan for, cope with and adapt to climate
change.
Results
Matching precipitation data with human observations
The collated human observations that pertain to precipitation changes
total 3753 and cover 129 countries, with 1827 localities across seven
Ecozones (Fig. 1a). At greater than 60% of these locations, multiple
precipitation changes were observed, such as the co-occurrence of
increased droughts and altered rainfall patterns (hotspots of changes
are shown in Supplementary Fig. 1). The most reported observations
(N= 1114) are about change in patterns of rainfall followed by changes
in amounts of rainfall (Supplementary Table 2). Altered patterns and
predictability of rains are less commonly reported in the Northern
Hemisphere and are mostly reported for tropical and subtropical
regions of Africa and Asia, where precipitation generally has a more
defined seasonality2,23 (Fig. 1b, c).
Of the locations with sufficient instrumental data, many human
observations show agreement with precipitation indices both globally
and regionally (Figs. 1aand2and Table 1) although we could not reject
the null hypothesis of independence between the two datasets using a
Pearson’s Chi-squared test (Supplementary Fig. 2). Globally, we find
that observations match better with the metrics that intuitively
describe the observed phenomenon (Table 1and Supplementary
Table 1). For example, we observe strong agreement between obser-
vations of changes in drought and the Palmer Drought Severity Index
(PDSI) (76%), in extreme rainfall and the R95p index (81%), and in
observed changes in general rainfall and Total Precipitation (TP)(72%).
We note that there is agreement whether we are considering metrics of
gradual change (TP, PDSI) or metrics of changes in extreme climate
events (R95p), suggesting that humans capably detect changes in both
the mean and the extremes independently24 (Fig. 2and Supplementary
Figs. 3–5).
At regional levels (Fig. 1a), agreement between human observa-
tions and instrumental precipitation metrics is better in Ecozones
where respective climate impacts are common. For example, drought
and PDSImatch best in the Afrotropicregion (91%);general rainfall and
TP match best in the Afrotropic and Palearctic regions (85% and 76%,
respectively), and extreme rainfall and R95p match best in the Indo-
Malay region (87%). In the Afrotropic and Palearctic regions, the
amounts of rainfall over specific periods might be relevant for agri-
culture or animal husbandry (i.e., greening of pastures), which could
explain the high match of human observations for total precipitation
or drought in these regions. The IndoMalay region encompasses
countries that are heavily influenced by seasonal precipitation as part
of the southeast monsoon, and in these areas, subsistence-oriented
people are likely to depend on (and therefore monitor) the timing and
intensity of heavy monsoon rains. These findings are consistent with
previous research (e.g., refs. 25,26) suggesting that people are more
attuned to rainfall changes that affect their activities most directly, and
that observations of these types of changes therefore tend to be the
most accurate.
We also compared the human observations with sub-periods
(1975–2005 and 1985–2005) within our full study period
(1955–2005) (Table 2and Fig. 3). We note the possibility of a
stronger alignment between observed and instrumental data as
the temporal scope of available data increases –at least for the
directly corresponding measurement indices (in bold in Table 2).
This increased alignment over longer time periods might suggest
that people’sobservationstendto‘average’the trends over long
periods of time. At least for annual rainfall (TP), this observation
seems to validate our assumption of 50 years as the period of
relevance for human observations, offering potentially useful
guidance for others who wish to incorporate human observations
into their data analyses. While the time span of human observa-
tions necessarily varies due to differences in year of data collec-
tion, starting period of the observed change and age of the
informants, these observations nevertheless document similar
changes in precipitation. One unexpected result is the fewer
number of matches (Table 2) for some indices in the 30-year time
series compared to the 20-year time series. We expected the
number to consistently increase as more data are available i.e.,
the ‘matches’for the 30-year time series should be at least as
many as for the 20-year time series. We suggest that this happens
because, for these indices, the direction of change is highly
variable for instrumental trends27, i.e., the sign of change switches
across the three different time series for several grid cells, which
is consistent with the increased unpredictability observed by
subsistence-oriented people.
Article https://doi.org/10.1038/s41467-024-53861-7
Nature Communications | (2024) 15:9563 2
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Fig. 1 | General overview of human observations of rainfall changes in com-
parison with instrumental observations of precipitation. A Ecozonal distribu-
tion of selected rainfall observation types made in subsistence-oriented
communities and their agreement with instrument-based metrics; (B) Distribution
of subsistence-oriented communities reporting changes in rainfall patterns (black
circles), superposed on the decadal trend in Dimensionless Seasonality Index
(DSI)2,22 (1955–2005). Solid grey lines indicate −23.5 and 23.5 degrees latitude
(Tropics). Dashed lines indicate −35 and 35 degrees latitude (Subtropics); (C)
Distribution of subsistence-oriented communities reporting changes in rainfall
predictability (black circles), superposed on the decadal trend in DSI2,22
(1955–2005). Solid grey lines indicate −23.5 and 23.5 degrees latitude (Tropics).
Dashed lines indicate −35 and 35 degrees latitude (Subtropics). TP Total Pre-
cipitation, R95p Annual total precipitation from days >95 percentile, CDD Con-
secutive Dry Days, PDSI Palmer Drought Severity Index. Human observations are
provided as source data.
Article https://doi.org/10.1038/s41467-024-53861-7
Nature Communications | (2024) 15:9563 3
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Comparing human observations with multiple indices can provide
additional information that is not evident when comparing with only
one index. In the IndoMalay, Nearctic and Palearctic regions, obser-
vations of extreme rains match with both R95p (extreme rainfall) and
CDD (dry conditions). In these regions, people report increases in
drought and extreme rainfall at the same locations. Since extreme
rainfall events are typically brief while drought is more prolonged, this
pattern suggests people are witnessing more short, intense rains
interrupted by longer dry periods. A second interesting feature of
these comparisons is that the drought perceived by local communities
does notalways match equally with measuresof relative dryness (PDSI)
and lack of rainfall (CDD) in different Ecozones. PDSI corresponds
more frequently with the human observations than does CDD. This
could be due to two reasons. First, since people in agrarian cultures
depend on soil conditions for food production, they likely have a
complex conceptualisation of drought4,28 that is captured better by the
PDSI index, which considers both precipitation and soil moisture.
Second, the sparseness of the CDD data drives our results (Table 1).
Relaxing the CDD resolution to the coarser HadEX2 dataset results in a
substantiallylarger numberof locations with overlapping data but with
more incomplete recording periods (Supplementary Fig. 4). The
HadEX3 dataset, instead, has a higher resolution and better data
coverage29 with a higher match with human observations of drought
(n= 631 with 70% match vs n= 199 and 44% match for GHCNDEX). This
difference could be explained partially by the fact that the rainfall data
that come from these datasets are assembled using largely indepen-
dent measuring stations to which different levels of quality control and
correction are applied29. All these datasets, however, do not cover key
regions where drought is relevant (i.e., sub-Saharan Africa) (Fig. 2cand
Supplementary Fig. 4) and where people are observing it. Our results
suggest that many subsistence-oriented communities tend to inte-
grate the impacts of climate change on precipitation and soil moisture,
but also that, in the absence of instrument-based data, human obser-
vations provide a first-order interpretation of ongoing changes at local
levels.
What we can learn from human observations
Our comparisons reveal some spatially distinct patterns in human
observations and trends in precipitation indices (Fig. 2and Supple-
mentary Figs. 3–5). We compared human observations of general
rainfall with measured trends in total surface precipitation, and we
identified mismatches between these two sets of data (Fig. 2a, purple
dots) primarily in Eastern Africa and the Eastern part of the Indian
peninsula in Southeast Asia. We also found mismatches between
observations of drought and trends in PDSI in these two areas (Fig. 2d),
along with a third hotspot of disagreement in Indochina. Data cover-
agefortheotherdroughtindexCDDisquitesparse,yetthesame
three areas suggest similar disagreement between drought and
CDD (Fig. 2c).
Several factors could create these mismatches between human
observations and instrument-based indices of precipitation, some of
which apply to all regions, and some of which are region specific(i.e.,
Eastern Africa and the Eastern side of the Indian peninsula in Southeast
Asia). The first potential cause is a mismatch in temporal scale. In this
study, we assumed that human observations represent a period of
about fifty years and our comparison of sub-periods described above
appears to support this assumption, but these observations could
represent longer or shorter periods4. When considering precipitation,
for example, variability in patterns can depend strongly on the time
period chosen. The IPCC report3outlines a general increase of pre-
cipitation rates in the Tropics over the first decade of the 21st century,
but a drying trend before then. As a result, due to these trends can-
celling each other out, the general global trend for many areas in this
latitudinal range shows no change or a minimal change between 1951
and 2008. This general global trend would be inconsistent with the
experiences of local communities where a variable climate is certainly
perceived.
A second potential reason for mismatches could relate to a sparse
cover of instrumental recording stations or incomplete recording
periods near the locations of human observations. The network of
stations contributing to the global rainfall data are unevenly
Fig. 2 | Geographical distribution of human observations that match (in black)
or do not match (in purple) with precipitation indices. A TP (GPCC version 7;
resolution: 2.5 × 2.5) with human observations of rainfall; (B)R95p(GHCNDEX;
resolution: 2.5 × 2.5) with human observations of extreme rainfall; (C)CDD
(GHCNDEX; res. 2.5 × 2.5)with human observations of drought; (D) PDSI(Dai et al.21;
resolution: 2.5 ×2.5) with human observations of drought. Note: there is a match if
the sign of the climate trend at each location is consistent with the sign of the
human observation. TP Total Precipitation, R95p Annual total precipitation from
days >95 percentile, CDD Consecutive Dry Days, PDSI Palmer Drought Severity
Index, GPCC Global Precipitation Climatology Centre, GHCNDEX gridded tem-
perature and precipitation climate extremes indices from the Global Historical
Climatology Network (GHCN) dataset, HadEX3 dataset of gridded station-based
climate extremes indices. Human observations are provided as source data.
Article https://doi.org/10.1038/s41467-024-53861-7
Nature Communications | (2024) 15:9563 4
Content courtesy of Springer Nature, terms of use apply. Rights reserved
distributed3, a challenge for local comparisons with precipitation
behaviour. For instance, for all the measurement indices best corre-
sponding with human observation types (numbers in bold in Table 1
and Table 2) that have more than 500 instances where human obser-
vations and instrumental observations overlap (numbers in brackets in
Tables 1and 2), matches are high (mostly above 60%). The HadEX2
dataset contains large data gaps in Africa, which have only partially
been covered with the HadEX3 dataset27,30 and several meteorological
stations have incomplete time series of precipitation data in Eastern
Africa2. This region corresponds with a clustering of mismatches
between human observations and instrumental data, as well as docu-
mented mismatches between simulated and instrumental precipita-
tions patterns, termed the “Eastern African Paradox”31. Similarly, the
GHCNDEX dataset includes a very limited number of gauging stations
in the Himalaya region29 allowing a comparison only with distant
instrumental observations that might not reflect local changes in
rainfall. Particularly in such regions with very limited instrumental data
the human observations can provide valuable insights.
A third reason is that local, non-climatic factors, such as com-
plex topography, microscale rain-shadows and other contributors to
microclimates can create highly localised patterns of precipitation
that are difficult to match when instrumental stations are located at
a distance from the community. For example, the impact of local
topography and inland water basins32 (i.e., Lake Victoria in Eastern
Africa, the Himalaya in Asia) could hinder matches between human
observations and instrument-based records that, while seemingly
close, are detecting very different patterns of precipitation. Addi-
tionally, in some cases, other anthropogenic factors (i.e., defor-
estation in Eastern Africa, dust storms in some Asian regions) might
have changed precipitation patterns locally33,34. but are not detected
due to the low density of gauging stations. For this analysis, in some
cases we had to compare human observation with instrumental
observations at hundreds of kilometres of distance. This effect is
evident in the different percentages of matches when we consider
different datasets or the same datasets at different resolutions (see
Table 1and Supplementary Figs. 3–5). Furthermore, most of the
mismatches are located on the margins of areas where there is
agreement and a shift from wetter toward drier, possibly indicating
that drier conditions are more extensive than suggested by the
instrumental data.
A fourth possibility is that humans and instrument-based indices
are simply observing different features of precipitation. It is possible
that people conceptualise a specific change in rainfall differently than
the same precipitation characteristic measured by the indices we
consider here. People may perceive “lack of rain”as strictly related to
the timing or duration of rainfall relevant to the farming calendar (see
also Savo et al.4). Indeed, Funk et al.32 showed that the amounts of
precipitation changed over the various seasons in Eastern Africa (one
of the regions where we had a clustering of mismatches), and many
communities in this area also reported changes in patterns and
increased unpredictability of rainfall (Fig. 1c). Insufficient rainfall dur-
ing the growing season will likely be interpreted as “lack of rain”by a
farmer, regardless of rainfall amounts during the other seasons. Thus,
while the matches between human observations and precipitation
indices can confirm the value of local knowledge, the mismatches can
highlight regions where indices are not conveying information that is
meaningful for local communities. This suggests that comparison with
human observations provides an added value to the assessment of
ongoing changes inprecipitation patterns (and theirimpacts), and that
regions where people are observing different trends in rainfall than
those depicted by coarse global avera ging of precipitation data require
finer characterisation.
Unpredictability and changing patterns of rainfall
The increased unpredictability and variability of rainfall patterns35
(1114 out of 3753 observations; Figs. 1b, c and 4) is a recurrent human
observation that is not easily captured by conventional precipitation
indices. Our visual comparison between the changes (decadal trend) in
the annual Relative Entropy, annual rainfall, Dimensionless Seasonality
Index (DSI)2,22 and human observations of changes in rainfall patterns
(Fig. 4),illustrates a prevalence of seasonality changes over the last 50
years2. However, one factor not fully captured by the DSI or other
indices is the increased unpredictability of rainfall which pertains, in
the context of this paper, to the usual start, ending and persistence
(continuous rains vs rains interrupted by dry spells) of the rainy season.
In this sense, unpredictability partially corresponds to the interannual
Table 1 | Percent agreement between human observations of rainfall changes and instrument-based precipitation indices for
the globe
Measurement Index, Source & Spatial
Resolution
Human Observations & Matches
Drought
Extreme Rainfall
General Rainfall
N = 835
N= 711
N= 840
Total Precipitation
(TP)
GPCC (2.5x2.5)
71% (835)
38% (709)
72% (840)
GPCC (0.5x0.5)
64% (835)
45% (708)
69% (839)
Consecutive Dry Days
(CDD)
GHCNDEX (2.5x2.5)
44% (199)
68% (190)
58% (183)
HadEX3 (1.875x1.25)
70% (631)
38% (566)
66% (654)
Palmer Drought
Severity Index (PDSI)
Dai et al.21 (2.5x2.5)
76% (825)
32% (651)
71% (803)
Annual total
precipitation from days
> 95 percentile (R95p)
GHCNDEX (2.5x2.5)
30% (158)
81% (150)
41% (137)
HadEX3 (1.875x1.25)
46% (517)
57% (496)
50% (511)
Human observations were compared with the closest available climate observat ion,an dwe id entified a match if the sign of the climate trend was consistent with the signof the human observation.
Percentagreement (%) andthe number of human observations within 250 km of instrumental observationsfor each measurementindex/data source are shown (inparentheses).High, medium and
low agreement is colour coded for ease of interpretation: dark grey (>65% match), light grey (between 65% and 35%), and white (<35%.). The database’s total number of human observations for
Drought,Extreme Rainfall and GeneralRainfall are also shown (N). Observed instrument-basedtrends are from 1955to 2005. Values in boldindicate measurement indices best corresponding with
humanobservation types(i.e., indicesof lack of rainfalland relative drynesswith observations of increaseddrought).GPC C Global Precipitation Climatology Centre,GHCNDEX griddedtemperature
and precipitationclimate extremesindices fromthe Global Historical Climatology Network(GHCN) dataset, HadEX3 dataset of griddedstation-based climateextremes indices. Human observations
are provided as source data.
Article https://doi.org/10.1038/s41467-024-53861-7
Nature Communications | (2024) 15:9563 5
Content courtesy of Springer Nature, terms of use apply. Rights reserved
variability of rainfall36but also to the fact that people are no longer able
to use critical seasonal clues (e.g., interpretations of wind direction-
ality and cloud formations) to predict the arrival or behaviour of
rainfall, which hinders planning activities such as planting and harvest
(Table 3). Predictability also pertains to the intensity of rainfall events,
with short, intense rains and longer dry periods, or alternation
between years with low and high amounts of rainfall (Table 3).
Observations about the increasingly unpredictable behaviour of rain-
fall are almost exclusively located in areas with strong seasonality
between latitudes of 30˚Nand30˚S(Fig.4). These areashave a strong
seasonality that now, according to many subsistence-oriented com-
munities, is increasingly unpredictable (according to traditional
knowledge systems).
Many human observations indicate that rains are delayed and
tend to end earlier, leading to a shortening of the rainy season. Shorter
rainy seasons are especially reported in Africa, corroborating the large
number of human observations of increased drought for this region.
These changes in Africa are supported by literature (i.e., refs. 31,37)
that also reports that most of the African tropics is experiencing
decreasing amounts of rain over increasingly variable periods. Such
Table 2 | Percent agreement between human observations of rainfall changes and instrument-based precipitation indices for
the globe over subsets of the analysed time period
Measurement Index, Source & Spatial
Resolution
Human Observations & Matches for Drought
(N=835)
1975-2005
1985-2005
1955-2005
Total Precipitation
(TP)
GPCC (2.5x2.5)
65% (541)
67% (563)
71% (835)
GPCC (0.5x0.5)
62% (519)
66% (551)
64% (835)
Consecutive Dry Days
(CDD)
GHCNDEX (2.5x2.5)
37% (74)
41% (82)
44% (199)
HadEX3 (1.875x1.25)
62% (643)
50% (631)
70% (631)
Palmer Drought
Severity Index (PDSI)
Dai et al.21 (2.5x2.5)
62% (515)
62% (510)
76% (825)
Annual total
precipitation from days
> 95 percentile (R95p)
GHCNDEX (2.5x2.5)
47% (74)
55% (87)
30% (158)
HadEX3 (1.875x1.25)
29% (516)
47% (517)
46% (517)
Measurement Index, Source & Spatial
Resolution
Human Observations & Matches for Extreme
Rainfall (N=711)
1975-2005
1985-2005
1955-2005
Total Precipitation
(TP)
GPCC (2.5x2.5)
45% (318)
40% (287)
38% (709)
GPCC (0.5x0.5)
44% (311)
44% (310)
45% (708)
Consecutive Dry Days
(CDD)
GHCNDEX (2.5x2.5)
75% (142)
67% (127)
68% (190)
HadEX3 (1.875x1.25)
44% (568)
51% (570)
38% (566)
Palmer Drought
Severity Index (PDSI)
Dai et al.21 (2.5x2.5)
45% (290)
36% (237)
32% (651)
Annual total
precipitation from days
> 95 percentile (R95p)
GHCNDEX (2.5x2.5)
47% (70)
47% (70)
81% (150)
HadEX3 (1.875x1.25)
67% (495)
45% (495)
57% (496)
Measurement Index, Source & Spatial
Resolution
Human Observations & Matches for General
Rainfall (N=840)
1975-2005
1985-2005
1955-2005
Total Precipitation
(TP)
GPCC (2.5x2.5)
65% (542)
65% (545)
72% (840)
GPCC (0.5x0.5)
63% (528)
67% (562)
69% (839)
Consecutive Dry Days
(CDD)
GHCNDEX (2.5x2.5)
55% (100)
47% (86)
58% (183)
HadEX3 (1.875x1.25)
60% (655)
44% (655)
66% (654)
Palmer Drought
Severity Index (PDSI)
Dai et al.21 (2.5x2.5)
66% (531)
61% (486)
71% (803)
Annual total
precipitation from days
> 95 percentile (R95p)
GHCNDEX (2.5x2.5)
59% (79)
67% (92)
41% (137)
HadEX3 (1.875x1.25)
34% (512)
48% (511)
50% (511)
Percentagreement (%) andthe number of human observations within 250 km of instrumental observationsfor each measurementindex/data source are shown (inparentheses).High, medium and
low agreement is colour coded for ease of interpretation: dark grey (>65% match), light grey (between 65% and 35%) and white (<35%.). The database’s total number of human observations for
Drought,Extreme Rainfalland General Rainfall are also shown(N). Values in boldindicate measurement indicesbest corresponding with human observation types (i.e.,indices of lackof rainfall and
relative dryness with observations of increased drought). Human observations are provided as source data.
GPCC GlobalPrecipitationClimatology Centre, GHCNDEXgridded temperature and precipitationclimate extremes indices fromthe Global Historical Climatology Network (GHCN)dataset, HadEX3
dataset of gridded station-based climate extremes indices.
Article https://doi.org/10.1038/s41467-024-53861-7
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Fig. 3 | Aggregated percent agreement in gridboxes for three time periods
between human observations of rainfall changes and annual Total
Rainfall (TP). A Human observations and TP calculated for the period 1955–2005;
(B) Human observations and TP calculated for the period 1975–2005; (C)Human
observations and TP calculated for the period 1985–2005. Interestingly, there are
fewer gridboxes with agreement for the shorter time series (1985–2005) and some
shifting of gridboxes between the bottom two panels (1975–2005 and 1985–2005),
but overall not much change in the percent agreement. Note: Human observations
are provided as source data.
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extended droughts have detrimental consequences for rain-fed agri-
culture, resulting in food insecurity for many subsistence-oriented
communities.
Our results suggest that, in addition to changes in total pre-
cipitation, the timing, intensity and distribution of rainfall are of most
concern for many communities and critical for their subsistence
activities. Thus, developing precipitation indices that document this
increased variability also becomes critical. Several researchers have
analysed the onset of monsoons or rainy seasons using different
methods (e.g., refs. 2,23,35,38,39), but the results and implications are
not always consistent or straightforward. While the Dimensionless
Seasonality Index can detect changes in the seasonality of rains, it does
not determine to what extent a change is significant. An improved and
integrated application of this index could result in a better under-
standing of current and future changes in the seasonality of pre-
cipitation, which could be used to plan agricultural practices
accordingly (i.e., incorporating crop varieties that tolerate dry sowing
or inconsistent rains). However, there are still no analyses, elabora-
tions, or indices built from instrumental data that can solve the
unpredictability issue. We recommend that the scientificcommunity
focus research on better understanding this increased variability and
unpredictability of rainfall, which is already threatening the sub-
sistence activities of thousands of communities globally.
The way forward
This work highlights rainfall changes of major concern for local
subsistence-oriented communities. Precipitation changes are difficult
to understand at present and harder to project under future green-
house gas forcing because they are affected by thermodynamic and
dynamic effects that are still very uncertain40; in fact, in precipitation
projections, model uncertainty is a very relevant factor11. The impacts
of these changes are already clear to local communities, but their
observations are still largely neglected.
Our data show that local knowledge can offer an additional and
robust way of observing climate change (see also18,41,42)providing
insights that can inform research in three important ways. First,
humans often observe multiple changes in rainfall patterns at one
location and often associate changes with other environmental indi-
cators (e.g., prevailing wind direction); as such, their ecological
knowledge could aid in designing multifactor indices of rainfall
behaviour. Second, our analysis shows that local observations high-
light areas where changes are already occurring, but insufficient spa-
tiotemporal coverage by instrument-based data often limits
quantification of those changes. Finally, many communities have
identified the unpredictability of rainfall as an emerging problem for
planning subsistence activities, as also mentioned in the latest IPCC
report15. Our work emphasises a need for more applicable precipita-
tion indices describing the seasonality and unpredictability of rainfall,
which matters most to thousands of communities around the world.
This knowledge could contribute to improving climate services and
the use of sub-seasonal to seasonal forecast in agriculture and other
sectors43,44.
Methods
This paper combines qualitative and quantitative data on precipitation
amounts and distribution. The methods used to gather and analyse
those two different types of data are reported below.
Qualitative data
The observations by subsistence-oriented communities were collated
between 2012 and 2013 through an extensive review of peer reviewed
and grey literature (e.g., scientific articles, project reports, participa-
tory videos)4. The sources used in this article date between 1994 and
2013; however, the documented observations reflect precipitation
changes that occurred up to 30-40 years before the interviews
reported in the reviewed studies. The age of informants as well as the
starting period of the observed change also varied. However, the
observations tend to agree about the direction of the observed change
for changes that began occurring around the middle of the 20th cen-
tury. We only considered sources in English or translated into English
and only those reporting direct observations by subsistence-oriented
communities (e.g., fishers, agro-pastoralists, Inuit, Aymara) thathave a
close relationship with their environment. We used a variety of key-
words such as climate change, traditional ecological knowledge,
interviews, observations adding the name of each country in the world
(250 including overseas territories). We collated a total of 1017 studies
(the complete list is freely available as a Supplementary material in
Savo et al.4) that covered multipleobservations of climate change (e.g.,
Fig. 4 | Distribution of the observations of rainfall predictability. The maps on
the left (and on the background on the right) show the (A) Annual R elative Entropy;
(B) Annual Rainfall; and (C) DSI (Dimensionless Seasonality Index)2,22 calculated
over the period 1955–2005 as decadal trends. Note: Only values around zero
indicate no or minor changes. “Match”means that the humanobservation category
“Rainfall - increased variability/changed patterns”matched the direction of the
indexesfor the plots on the right. Human observations are provided as source data.
Article https://doi.org/10.1038/s41467-024-53861-7
Nature Communications | (2024) 15:9563 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved
changes in winds, changes in phenology, changes in floods), and
aggregated several categories to examine general trends in the
observations (an overview of the full dataset is provided in Savo et al.4).
In this paper, we focused only on five detailed categories related to
precipitation (Supplementary Table 2). In total, these categories
included 3753 observations globally (provided as source data). We did
not compare snowfall observations with quantitative indices because
of the low number of human observations (253), the limited availability
of instrument-based measures of only snowfall,and frequent inclusion
of snowfall as a component of total precipitation20.
Although the aim of our paper is not to validate the human
observations, we discuss some of the potential biases that could be
related to these data. The first-hand observations by subsistence-
oriented communities were collected by independent researchers
using different interview methods. We only included direct observa-
tions by subsistence-oriented people, excluding the information
reported by researchers, rangers, or other actors. The observations
were gathered in different times of the year through questionnaires,
semi-structured interviews, focus group discussions, participatory
videos etc. (e.g., refs. 45,46). Questions were also different, spanning
from generic questions (have you noticed any change in the environ-
ment?) to specific questions (have you noted a decrease/increase in
rainfall?). However, since our dataset includes observations of diverse
changes from a variety of localities across the globe, we believe that
our data have significance beyond the diversity of data collection
methods and seasons, typology of questions and researchers’
approaches, and culture and gender of the informants4. Thisis because
we found agreement among observations collated by different
researchers with different methods in close vicinity, and becausein the
studies participants could report changes in both directions (e.g.,
increase or decrease of rainfall). Finally, many researchers have verified
the observations in the collated case studies comparing them with
local climatic data (e.g., refs. 47,48). For these reasons, we can identify
patterns in the observations that are likely to represent actual changes
in precipitation amounts and behaviours.
We compiled all data into a single database. Each observation was
characterised by the country and locality where the study was based,
subsistence activity of the observers, details about the observed
change (e.g., “the highest rainfalls used to be in June or July, whereas
now, they occur in September”49) and the bibliographic source. Each
observation was georeferenced using the name of the locality and the
open source Geonames geographical database (http://www.
geonames.org) (see also4). For the observations of changes in rainfall
patterns, we detailed if communities were noticing an early or delayed
start and end of rains as well as changes in duration or predictability of
the rainy season.
Quantitative data
We compared the qualitative data to quantitative observations of five
different climatic indices (Supplementary Table 1) that were estimated
as decadal trends from instrument-based records for a period of 50
years (1955–2005)andsubsetsofthisperiod(1975–2005 and
1985–2005).
(a) We selected this period because it is consistent with the analyses
reported in Savo et al.4and aligns with the period over which we
assumed human observations apply. We recognise that the actual
time scale of human observations is not exact, but we note that
subsistence-oriented communities generally observed these
changes over one-to-two generations4.
(b) The five quantitative climatic indices included: (1) monthly Total
Precipitation (TP); (2) extreme precipitation as defined by the
annual total precipitation from days > 95 percentile (R95p); (3)
drought as defined by Consecutive Dry Days (CDD), or the num-
ber of consecutive days where precipitation is less than 1 mm/day;
(4) drought as defined by the Palmer Drought Severity Index
(PDSI), which incorporates measures of precipitation and poten-
tial evapotranspiration into a hydrological accounting system; (5)
a global measure of the Dimensionless Seasonality Index (DSI),
which provides a measure of the seasonal distribution of rainfall,
weighted by the normalised mean annual rainfall. Changes in DSI
can indicate either a variation in annual rainfall or seasonal dif-
ferences in rainfall amounts. The Relative Entropy (RE), which is a
component of DSI5, provides a measure of the number of wet
months and the duration of the rainy season within unimodal
rainfall regimes (Fig. 4; Supplementary Table 1).
(c) We calculated linear, decadal trends for the five climatic indices
listed in Supplementary Table 1 (TP, CDD, PDSI, R95p, DSI) using
available datasets at various resolutions (i.e., GHCNDEX, HadEX2,
HadEX3). The details and sources of the various datasets are pro-
vided in Supplementary Table 1. Most datasets were available at a
resolution of 2.5 ×2.5 degrees. Where possible, we used this reso-
lution for consistency and comparability, and we provide as sup-
plementary files the decadal trends at coarser resolutions
(Supplementary Figs. 4 and 5). Note that the recent HadEX3 dataset
for the CDD and R95p indices was available at a resolution of 1.875
×1.25 degrees and DSI was available in 0.5 × 0.5 and 1 × 1 degree
resolutions (we showed the 0.5 × 0.5 resolution in Fig. 4). Unlike
Feng et al.2, we calculated the decadal trend instead of annual
changes in the index to remain consistent with our other datasets.
Data analyses
For the instrument-based datasets, we estimated linear decadal
trends for sites that contained at least 25 years of data for the period
Table 3 | Details of changes in rainfall predictability extracted from four selected case studies in the hotspots of rainfall change
(Supplementary Fig. 1)
Locality Changes in rainfall predictability and their consequences
Mvumi Division, Dodoma region, Tanzania Farmers base their decisions about farming on selected signs and indicators (stars, fog, fruit production from
specific trees, wind, the onset of first rains) for bad and good rainy seasons, but now predictions of rainfall are not
always as successful as they were before55.
Palca village, Pedro Domingo Murillo, Bolivia The rainy season is delayed with a decreased amount of precipitation. Now that there is less snow on the sur-
rounding mountains and less rain, there is also less water in the streams and farmers’irrigation channels. Rain, hail
and frost events are less predictable than in the past. Rains are now more intense and last for shorter periods of
time, sometimes are mixed with hail, while before the rain was gentle. Hail is falling at unexpected times56.
Gomoa‐Akotsi village, Gomoa East dis-
trict, Ghana
Rainfallhas become highly variable and unreliable, with eithertoo much rain (floods) or too little rain/shorter rainy
seasons (droughts). Farmers perceive that there are more years with less rain than years with excessive rains.
Instrumental data used in Yaro’s57 analysis show a large divergence between years with low and high amounts of
rains,supporting people’s observations.Variability ofrainfall has alsobeen observedwithin the yearand among the
seasons, which are now less distinct57.
Virudhunagar district, Vaippar basin, Tamil
Nadu, India
Reduction in rainydays over the pastthree decadeswith an increasein the intensityof rainfall events, resulting in an
alternation of floods and droughts. Rainfall has become unpredictable, and the monsoon starts later. These
changeshave caused a reduction in the growing seasons from three totwo, with a shift of the cropping calendar58.
Article https://doi.org/10.1038/s41467-024-53861-7
Nature Communications | (2024) 15:9563 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1955–2005, 15 years of data for the period 1975-2005 and 10 years of
data for the period 1985-2005 using the R statistical software
package (www.R-project.org) to calculate ordinary least squares
regression50. For the HadEX2 dataset we instead estimated linear
decadal trends for locations with at least 10 years of data to increase
the data coverage; data are only presented in the Supplementary
files (i.e., Supplementary Table 3 and Supplementary Figs. 4 and 5).
Time series are complete for the Dai et al.21 and the GPCC datasets,
while for the HadEX3 and GHCNDEX datasets, between 81% to 89% of
time series had more than 90% of valid data (source data). After
estimating trends for each location with sufficient data, we coded
the direction of the trends so that they could be compared with the
qualitative data of human observations. Specifically, increasing
trends in wetness (or precipitation) were positive, and decreases in
wetness (or precipitation) were negative. The magnitudes of the
specific trend and their significance were not a priority; we focused
instead on the direction of the general trends and their relationship
with human observations. Thus, although there is the potential for
temporal autocorrelation in the monthly TP data, we did not remove
the seasonal cycle because previous research has suggested that the
seasonality will not significantly affect the observed direction of the
trend (i.e., increasing or decreasing rainfall)51. We performed a
Pearson’s chi-squared test with Yates continuity correction52 in R to
test whether there was a correlation between rainfall data (TP) and
human observations.
We also tested whether there was a change in correspondence
between human observations and rainfall data across time (Fig. 3). For
each period shown in the three panel Fig. 3,wehave:
1. Created a gridbox raster where raster values are the average of all
decadal trend values within 10 cells of the original raster cells
(horizontally and vertically, and we used the centroid of each
grid cell).
2. Compared the sign for the average values obtained with the sign
for all human observations that occur within each gridbox to
assign “same”or “different”categories to human observations.
3. Created separate rasters for: (a) all human observations, (b)
human observations that agree about increased precipitation, c)
human observations that agree about decreased precipitation,
where the raster values are counts of points that occur inside the
gridboxes.
4. Obtainedthepercentageofagreementaboutincreased or
decreased precipitation within each gridbox, dividing human
observations that agree about increased precipitation and the
total of human observations (b/a from point 3) and dividing
human observations that agree about decreased precipitation and
the total of human observations (c/a) and we have merged these
agreements into one raster.
5. Finally, we havedisplayed the rasters as coloured gridboxes based
on specified bins.
We compared human observations with instrument-based data
globally, and then regionally (by Ecozone). We referenced the latitude
and longitude of each human observation site with shapefiles doc-
umenting the terrestrial ecoregions of the world53 made freely avail-
able by the World Wildlife Fund (http://www.worldwildlife.org/
publications/terrestrial-ecoregions-of-the-world), overlapping the
continent shapes54. For each human observation, we identified the
closest available instrumental observation and compared the sign of
the climate trend at this location with the sign of the human obser-
vation (e.g., increasing precipitation to increasing rainfall). We then
recorded if the sign of the trends matched. We performed this com-
parison for every combination of human observations (Supplementary
Table 2) and climatic indices (Supplementary Table 1) available except
for DSI for which we only performed a visual comparison. To ensure
that human observations were not being compared to geographically
irrelevant climate observations, we only analysed the sites where the
nearest instrumental observation was within 250 km of the human
observation with which it was being compared. To test whether
250 km was too coarse for regions with complex topography or varied
microclimates, we also investigated smaller distances (50 km, 100 km).
However, these distances resulted in a substantial loss of qualitative
data (i.e., observations) not close enough to instrumental observa-
tions. Finally, we used different datasets at different resolutions
(Supplementary Table 1) to assess if matches and mismatches were
possibly affected by the granularity of instrument-based data.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The data about people observations are included in this article, in the
supplementary information files and as source data. The instrumental
observations were downloaded from the websites provided in Sup-
plementary Table 1. Data completeness for the GCHNDEX and HadEX3
datasets (R95p, CDD; 1955–2005) are included as source data. Source
data are provided with this paper. Correspondence and requests for
other data should be addressed to V.S. Source data are provided with
this paper.
Code availability
Correspondence and requests for materials should be addressed
to V.S.
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Nature Communications | (2024) 15:9563 11
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Acknowledgements
Many thanks are due to the Hakai scholars for their suggestions and
comments. We are gratefulto Domenico Fabio Savo for his help with the
R code troubleshooting and to Vidur Mithal for his help with the HadEX3
datasets. V.S. was supported by the Government of Canada/avec l’appui
du gouvernement du Canada, the Tula Foundation (Heriot Bay, BC,
Canada) through the Hakai Institute (Heriot Bay, BC, Canada) and the
Department of Education Science, University Roma Tre. K.E.K. was
supported by the NSERC Canada Research Chair programme and
NSERC Discovery Grant R832686. J.S. and A.S.H. were supported by the
Research Council of Norway grant 244551/E10 (CiXPAG). J.S. further
acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation) under Germany’s Excellence Strategy -
EXC 2037: “CLICCS-Climate, Climatic Change, and Society”- Project
Number: 390683824, contribution to the Center for Earth System
Research and Sustainability (CEN) of Universität Hamburg. C.L.Q. was
supported by the UK Royal Society (Grant RP\R1\191063).
Author contributions
Both V.S. and K.E.K. conceptualised the research design and contributed
equally to the writing of the manuscript. V.S. conducted the biblio-
graphic search and performed the analyses of the ethnographic data.
J.S. provided support for the selection of climatic datasets and indices
and contributed to the writing of the manuscript. C.M., J.B. and A.S.H.
carried out the analyses of climatic data and contributed to the writing of
the manuscript. C.L.Q. and D.L. contributed to the writing of the
manuscript with valuable input.
Competing interests
The authors declare no competing interests
Additional information
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V. Savo.
Peer review information Nature Communications thanks James Ford,
Katharine Willett and the other, anonymous, reviewer for their con-
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