Anthropogenic amplification of precipitation variability over the past century
Editor’s summary
As climate change continues, warming of the atmosphere allows it to hold more water and thus produce more precipitation. A corollary to more rain is the amplification of precipitation variability, a behavior easier to predict than to observe. Zhang et al. used global records of daily precipitation to show that this expected increase in precipitation variability is in fact detectable in the data over the past century. This trend, which is most prominent over Europe, Australia, and eastern North America, will make adaptation more difficult for societies and ecosystems. —Jesse Smith
Abstract
As the climate warms, the consequent moistening of the atmosphere increases extreme precipitation. Precipitation variability should also increase, producing larger wet-dry swings, but that is yet to be confirmed observationally. Here we show that precipitation variability has already grown globally (over 75% of land area) over the past century, as a result of accumulated anthropogenic warming. The increased variability is seen across daily to intraseasonal timescales, with daily variability increased by 1.2% per 10 years globally, and is particularly prominent over Europe, Australia, and eastern North America. Increased precipitation variability is driven mainly by thermodynamics linked to atmospheric moistening, modulated at decadal timescales by circulation changes. Amplified precipitation variability poses new challenges for weather and climate predictions, as well as for resilience and adaptation by societies and ecosystems.
Climate warming increases the near-surface atmosphere moisture holding capacity by around 7% per kelvin according to the Clausius-Clapeyron relation. The increased atmospheric moisture, as the first-order driver, fuels precipitating systems, leading to increased precipitation extremes—“when it rains it pours” (1–5). This means greater fluctuations between precipitation events and wider swings between wet and dry episodes, manifesting an amplified precipitation variability (6–13). The amplification of precipitation variability can occur across a variety of timescales, ranging from day-to-day, season-to-season, and year-to-year. This has further consequences for the hydrological cycle, for example, with an increased seasonal range in precipitation minus evaporation and aridity metrics (14–17). Such amplified variability can have profound impacts on human society and ecosystems, leading to less reliable water supplies, altered agricultural yields (18), disturbed ecosystem functioning and hence terrestrial carbon sinks (19), and disturbed economic growth (20). These potential consequences pose new challenges to climate adaptation and the resilience of society.
Climate models do project a robust increase in precipitation variability from daily to multiyear timescales under future global warming (6–13). This is expected to predominantly occur in climatologically wet regions, manifesting a “wet-get-more variable” paradigm (6, 8). The projected increase in precipitation variability results from a combination of increased atmospheric moisture and weakened circulation variability related to enhanced static stability of the troposphere and weakened large-scale overturning circulation under global warming (8, 9, 21–23).
However, it remains unclear whether precipitation variability has already been on the rise in observations, which hinders timely adaptation actions. The detection of observed change is made more difficult by large internal variability in daily precipitation, which complicates identification of the emergence of an anthropogenic signal. Although a very limited number of studies have reported an observed increase in precipitation variability at specific timescales for some regions (6, 24, 25), a systematic view across spatial-temporal scales is lacking. More importantly, the mechanisms for observed changes in precipitation variability are unclear. Here, we present a comprehensive understanding of observed precipitation variability change over the past century, addressing the facts, mechanisms, and anthropogenic contributions. We show that precipitation variability has amplified systematically over the past century from global to regional scales and across daily to intraseasonal timescales. This amplified variability is primarily driven by thermodynamics linked to atmospheric moistening, which is attributable to anthropogenic warming.
Observed amplification of precipitation variability over the past century
To examine the changes in precipitation variability, we used multiple observational datasets to account for observational uncertainty. These include five sets of global-scale and eight sets of regional-scale datasets of daily precipitation observations, which are generally consistent in representing climatological precipitation variability [see materials and methods in the supplementary materials; table S1 and fig. S1]. Precipitation variability is measured at a variety of timescales, including day-to-day (i.e., total variability), synoptic, monthly, intraseasonal, and interannual. It is estimated as the temporal standard deviation of precipitation, after the long-term trend and annual cycle have been removed (materials and methods).
From 1900 to 2020, day-to-day precipitation variability has increased over most land regions where observations are available according to the Global Historical Climatology Network (GHCN)–Daily dataset (26) (Fig. 1). Approximately 75% of land area has shown an increase, with a global mean trend of 1.2% per 10 years since 1900. In particular, the increasing trend is dominated by the later period since the 1950s (fig. S2). This is in line with the fact that global mean temperature increased more prominently during this period compared with the earlier period (27). Note that the availability of observational data varies with time and region. For example, the century-long trend shown in Fig. 1 for South Asia mainly represents the pre-1970s period, and that for East Asia represents the post-1950s period (fig. S3).
Fig. 1. Observed trend in daily precipitation variability from 1900 to 2020.
Trend in day-to-day precipitation variability according to GHCN-Daily. Linear trend is first computed for stations with long data records, then gridded to 1° by 1° boxes by taking the median of stational trends within each grid box (materials and methods). The trend is normalized with respect to the entire period for each station and expressed in percentage per 10 years. Blue boxes mark the subregions focused on in this study.
The increase in daily precipitation variability occurs in all four seasons with a similar global pattern, although seasonal differences do exist at small regional scales (fig. S4). This suggests a universal constraint for increasing precipitation variability throughout the year. Thus, we focus on changes in daily precipitation variability throughout the year in the analyses that follow.
Regionally, the increase in precipitation variability is most prominent over Europe, Australia, and eastern North America, areas for which dense and long-running observations are available (Fig. 2). According to the GHCN-Daily data, approximately 80, 63, and 89% of the land area shows an increasing trend over the past century in these three regions, respectively. The consistency across multiple observations confirms the robustness of the increasing trend. Nevertheless, in other regions, the long-term trend in precipitation variability is less prominent, either because of strong interdecadal variability or because of inconsistency among the datasets (fig. S5).
Fig. 2. Regional change in daily precipitation variability from 1900 to 2020.
Time series of precipitation variability in multiple observational datasets (details in table S1) for Europe (A), Australia (B), and eastern North America (C). Regional mean precipitation variability is normalized with respect to the entire period for each dataset and expressed as percentage anomaly. See blue boxes in Fig. 1 for region definitions.
As precipitation variability exists at a variety of timescales, which are associated with different weather and climate patterns, it is important to understand the responses at these different timescales. Interestingly, over the three regions (Europe, Australia, and eastern North America) where the total day-to-day precipitation variability has substantially increased, the synoptic, monthly, and intraseasonal variability show consistent increases (fig. S6). Thus, the observed increase in day-to-day precipitation variability is contributed jointly by the synoptic, monthly, and intraseasonal variations. Nevertheless, for interannual precipitation variability, no significant trend has emerged from the strong interdecadal variability (fig. S6). Given the consistent increases across timescales from daily to intraseasonal, we focus on the daily precipitation variability to understand the physical drivers of the observed change.
Physical drivers for amplified precipitation variability
Changes in precipitation, largely through moisture advection, can be affected by changes in atmospheric moisture (i.e., thermodynamics), changes in atmospheric circulation (i.e., dynamics), or both (i.e., nonlinear effects). This can be diagnosed using a moisture budget (28, 29). Among all the moisture budget components, precipitation variability can be reasonably approximated by the variability in vertical moisture advection
(1)
This simplified framework provides a clear physical interpretation of the thermodynamic and dynamic processes. Specifically, the thermodynamic contribution is estimated as
We applied the moisture budget diagnostics to ECMWF Reanalysis v5 (ERA5) from 1940 to 2020 (30). ERA5 reproduces the observed increase in precipitation variability over the three regions (Figs. 2 and 3 and fig. S7B). Quantitatively, the magnitudes of trend are consistent between observations and ERA5 over Europe and eastern North America (compare gray bars and markers in Fig. 3, A and C). For Australia, although different datasets show notable spread in the magnitude of trend, which may be related to strong interdecadal variability in this region, they generally indicate an increasing trend (Figs. 3B and 2B). The reasonable capability in reproducing the observed increase in precipitation variability enhances the reliability of physical diagnostics based on ERA5.
Fig. 3. Physical processes for changes in precipitation variability.
Time series of precipitation variability (σP; gray), the variability of vertical moisture advection [
The long-term trend of precipitation variability is reasonably approximated by that of vertical moisture advection based on Eq. 1 at the global scale (fig. S7, A to D). Regionally, the temporal variations of the two terms also resemble each other, despite the slight differences in the magnitude of trend (compare gray and dark-blue curves and bars in Fig. 3). This demonstrates the validity of the simplified moisture budget framework.
We then disentangled the thermodynamic and dynamic effects. The increase in precipitation variability mainly arises from the steady increase in atmospheric moisture, which contributes to ~60% of the increasing precipitation variability over the three regions (Fig. 3, red curves and bars, and fig. S7, G and H). This suggests that even if atmospheric circulation remains unchanged, the increased atmospheric moisture alone will enable larger precipitation anomalies when it precipitates, hence leading to amplified precipitation variability.
On the other hand, the dynamic effect related to atmospheric circulation change, despite enhancing precipitation variability to some extent over the 1940–2020 period, exhibits obvious interdecadal variations (Fig. 3, light-blue curves and bars). In particular, the variability of vertical motion intensified before the 1980s but weakened afterward, over Europe and Australia (Fig. 3, A and B). Overall, the dynamic effect is less pronounced with larger spatial differences than is the thermodynamic effect (fig. S7, E and F). This is consistent with our current understanding that changes in circulation under global warming are more uncertain than changes in humidity owing to strong internal variability (31, 32). Besides, the nonlinear effect is generally small (Fig. 3, brown curves and bars).
The dominant role of atmospheric moistening in the observed increase in precipitation variability is consistent with that expected in future projections (8), suggesting that the thermodynamic response to climate warming has already emerged. This enhances our confidence in the physical understanding, which connects the past and future changes. The dominant contribution of atmospheric moistening also explains the consistent increases in precipitation variability over the past century across different seasons and timescales (figs. S4 and S6). In terms of dynamic effect, the leading factor is the projected weakening of the vertical motion variability associated with the large-scale overturning circulation that acts to suppress precipitation variability (8, 9, 21–23). Although this is not clearly seen in observations, because of strong interdecadal variability, there was a weakening trend in vertical motion variability since the 1980s over Europe and Australia (Fig. 3, A and B). This may be related to the weakening and poleward expansion of the subsiding branches of the Hadley cell observed since the 1980s, where both anthropogenic emissions and internal decadal variability play a role (33–35). On the other hand, increasing warming contrast between land and sea and the associated continental relative humidity decline could potentially lead to drier dry periods in some regions and, subsequently, enhance precipitation variability (36, 37). Regional dynamics are rather complicated, and region-specific studies are needed to fully understand them.
Detecting anthropogenic fingerprints
As the observed increase in precipitation variability is dominated by increased atmospheric moisture, this suggests an important role of anthropogenic climate warming. Can we therefore explicitly detect anthropogenic fingerprints in observed precipitation variability? We examined the effects of different external forcings based on multimodel simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) (38). Combined and individual forcings are considered, including all external forcings (“ALL,” including both anthropogenic and natural), natural forcing alone (“NAT”), greenhouse gas forcing alone (“GHG”), and anthropogenic aerosol forcing alone (“AA”) (materials and methods; table S2).
Under ALL forcings, precipitation variability increases globally except for in some subtropical regions (Fig. 4A). This results from the tug-of-war between GHG and AA forcings, in which the GHG-induced increases in precipitation variability dominate over the AA-induced decreases (Fig. 4, B and C).
Fig. 4. Anthropogenic influences on precipitation variability change.
(A to C) Linear trend of precipitation variability from 1900 to 2020 under all external forcings [ALL, (A)], greenhouse gas forcing [GHG, (B)], and anthropogenic aerosol forcing [AA, (C)]. Shadings show the multimodel ensemble mean trend, and dots indicate agreement of 10 out of 12 models on the sign of change. (D to F) Regional mean trend of precipitation variability under ALL, natural (NAT), GHG, and AA forcings, for Europe from 1932 to 2020 (D), Australia from 1901 to 2019 (E), and eastern North America from 1900 to 2020 (F). Box-whiskers show the 5th, 25th, 75th, and 95th of the multirun ensemble, and the white horizontal lines show the multimodel ensemble mean. Dashed gray lines denote observed trends from different observational datasets. Note that the periods analyzed for each region are determined by availability of observational records. Trends are normalized with respect to the entire period for each region and expressed as percentage per decade. (G to I) Scaling factors for 1-signal and 3-signal detection analyses for Europe (G), Australia (H), and eastern North America (I). In the detection analyses, the average of multiple observations with full time span is used in the regression model (i.e., GHCN-Daily and E-OBS for Europe, GHCN-Daily and AWAP for Australia, and GHCN-Daily for eastern North America). Dark bars indicate the best estimate of scaling factors and the 5 to 95% confidence intervals, with light bars indicating the confidence intervals with modeled noise doubled.
Regionally, over the three regions showing significant increases in precipitation variability, the observed increase can be reproduced by simulations with ALL and GHG forcings, despite the forced responses being generally weaker than observations (Fig. 4, D to F, and fig. S8). Precipitation variability shows no obvious change under NAT forcings and decreases under AA forcing (except for Australia) (Fig. 4, D to F).
To detect anthropogenic influence more unambiguously, we conducted an optimal fingerprinting detection and attribution analysis. This was achieved with a generalized multivariate linear regression model that compares the observed change with forced responses with consideration of internal climate variability (39–41) (materials and methods). Detection of a specific forcing can be claimed if the confidence interval of the scaling factor is significantly above zero.
In the one-signal detection, the effects of ALL and GHG forcings can be detected over the three regions with scaling factors significantly above zero, whereas the effects of NAT and AA cannot (Fig. 4, G to I). Considering correlations between signals, we then applied three-signal detection to further distinguish the influence of one forcing from that of the others. The effect of GHG can be separately detected from the effects of AA and NAT (Fig. 4, G to I). This clearly demonstrates that the observed increase in precipitation variability during the past century over the three regions is attributable to anthropogenic forcing and, more specifically, to GHG emissions.
It should be noted that, unlike the monotonic increase in response to GHG forcing, precipitation response to AA varies following regional emissions (42–44). This is seen from the empirical orthogonal function modes of forced responses to ALL and AA, where the leading mode represents the century-long trends, and the second mode corresponds to changing AA emissions in Europe and North America (fig. S9) (42). Despite time-varying AA influence, the dominant and attributable role of GHG in the observed amplification of precipitation variability is robust for Europe and eastern North America, as supported by the detection and attribution results for the 1950–2020 period (fig. S10). We also note that the signal is weak over Australia for the shorter period of 1950–2020 because of large internal variability (see time series in fig. S8).
The detectable role of GHG forcing further supports the process understanding based on moisture budget diagnostics. The physical reasoning in combination with optimal fingerprinting forms a more comprehensive understanding. That is, anthropogenic GHG emissions have led to climate warming and atmospheric moistening, which have acted as the main thermodynamic drivers leading to increased precipitation variability over the past century.
Discussion
Climate models project that precipitation variability will amplify under continuous global warming (6–13), yet it remains unclear whether the amplified precipitation variability has emerged in observations, which hinders timely adaptation actions. In this study, we report a systematic amplification of precipitation variability over the past century from global to regional scales and across timescales ranging from daily to intraseasonal, on the basis of multiple observational datasets. Approximately 75% of land area (with observations) has experienced an amplification of precipitation variability, with daily variability increased by 1.2% per 10 years globally. It is driven mainly by thermodynamics linked to atmospheric moistening, modulated at decadal timescales by circulation changes.
It should be noted that the quantification of trends is subject to the observational uncertainties arising from sources and the quality and temporal frequency of daily records. Strict criteria were applied for the selection of GHCN station data to ensure reliable estimates of daily variability with sufficient sampling frequency and time span. The global trends of precipitation variability have emerged largely from regions with dense and long observational records (including Europe, Australia, and eastern North America). Positive trends are also seen over other regions with relatively coarse observations, but quantitative assessments for these regions require caution.
The capability of ERA5 reanalysis in reproducing the observed long-term increases in precipitation variability makes it useful for process studies. Nevertheless, there are noticeable differences in decadal variability between ERA5 and observations to some extent (Fig. 2). This could partly be due to the variable quantity and quality of observations assimilated in ERA5, for example, before and after the inclusion of satellite observations since 1979, etc. (30).
Both mean precipitation and precipitation variability have increased over ~75% of land with observational coverage during the past century, with similar spatial patterns. The physical connections between changes in precipitation variability and mean state remain to be fully understood. The long-term trends are dominantly driven by monotonically increasing GHG forcing, modulated by time-evolving AA influences at decadal and regional scales with uncertainties attached. The consistency between observed and projected increase in precipitation variability enhances our confidence in global hydrological projections. For quantitative regional applications, one must keep in mind the uncertainties associated with potential intermodal spread, for example, biases in simulated hydroclimate trends over arid or semiarid regions (45).
The observed amplification of precipitation variability reflects the facts of intensified wet and dry extremes and potentially the wide and rapid swings between them—wet-dry whiplash as a type of sequential compound extremes (16, 46–48). Rapid and wide swings between climate extremes not only challenge existing capabilities of modern-day weather and climate prediction systems but also have cascading impacts on human society, with threats to the climate resilience of infrastructures, risk management, agriculture (18), ecosystem functions (19), and economic development (20). Our finding also has implications for human efforts toward net-zero emissions, as altered ecosystem functioning can affect terrestrial carbon sinks (19).
Acknowledgments
Funding: The study is jointly supported by the National Natural Science Foundation of China (41988101), the National Key Research and Development Program of China (2023YFF0805202, 2020YFA0608904), the National Natural Science Foundation of China (42275038), China Meteorological Administration Climate Change Special Program (QBZ202306), and the UK–China Research Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China.
Author contributions: Conceptualization: W.Z. and T.Z. Methodology and investigation: W.Z. Critical insights: P.W. and T.Z. Writing – original draft: W.Z. Writing – review & editing: W.Z., T.Z., and P.W.
Competing interests: The authors declare that they have no competing interests.
Data and materials availability: Daily precipitation observations can be acquired as follows: GHCN-Daily, https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily; REGEN-LONG, https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f6973_9398_8796_3040; CPC_Global, https://climatedataguide.ucar.edu/climate-data/cpc-unified-gauge-based-analysis-global-daily-precipitation; GPCC, https://www.dwd.de/EN/ourservices/gpcc/gpcc.html; MSWEP, https://www.gloh2o.org/mswep/; CHIRPS, https://climatedataguide.ucar.edu/climate-data/chirps-climate-hazards-infrared-precipitation-station-data-version-2; AWAP, https://eo-data.csiro.au/projects/awap/; E-OBS, https://www.ecad.eu/download/ensembles/download.php; CPC_CONUS, https://psl.noaa.gov/data/gridded/data.unified.daily.conus.html; CN05.1, https://ccrc.iap.ac.cn/resource/detail?id=228; and APHRO_MA, APHRO_ME, and APHRO_RU, https://www.chikyu.ac.jp/precip/english/downloads.html. ERA5 reanalysis data can be acquired from https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. CMIP6 model simulations are available at https://esgf-node.llnl.gov/search/cmip6/. The codes used in this paper are available in Zenodo (49).
License information: Copyright © 2024 the authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original US government works. https://www.science.org/about/science-licenses-journal-article-reuse
Supplementary Materials
This PDF file includes:
Correction (8 August 2024): Figures 1 and 4, A to C, have been updated. The only difference is that national boundaries on the background maps (previous map database provided by the NCAR Command Language software) have been removed. This does not affect the research results and conclusions.
References and Notes
1
K. E. Trenberth, Conceptual framework for changes of extremes of the hydrological cycle with climate change. Clim. Change 42, 327–339 (1999).
2
R. P. Allan, B. J. Soden, Atmospheric warming and the amplification of precipitation extremes. Science 321, 1481–1484 (2008).
3
P. A. O’Gorman, T. Schneider, The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. U.S.A. 106, 14773–14777 (2009).
4
E. Fischer, R. Knutti, Observed heavy precipitation increase confirms theory and early models. Nat. Clim. Chang. 6, 986–991 (2016).
5
H. Douville, K. Raghavan, J. Renwick, R. P. Allan, P. A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T. Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, O. Zoli, “Water cycle changes” in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, B. Zhou, Eds. (Cambridge Univ. Press, 2021), pp. 1055–1210.
6
A. G. Pendergrass, R. Knutti, F. Lehner, C. Deser, B. M. Sanderson, Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).
7
J. R. Brown, A. F. Moise, R. A. Colman, Projected increases in daily to decadal variability of Asian-Australian monsoon rainfall. Geophys. Res. Lett. 44, 5683–5690 (2017).
8
W. Zhang, K. Furtado, P. Wu, T. Zhou, R. Chadwick, C. Marzin, J. Rostron, D. Sexton, Increasing precipitation variability on daily-to-multiyear time scales in a warmer world. Sci. Adv. 7, eabf8021 (2021).
9
C. He, T. Li, Does global warming amplify interannual climate variability? Clim. Dyn. 52, 2667–2684 (2019).
10
R. R. Wood, F. Lehner, A. G. Pendergrass, S. Schlunegger, Changes in precipitation variability across time scales in multiple global climate model large ensembles. Environ. Res. Lett. 16, 084022 (2021).
11
K. Schwarzwald, A. Poppick, M. Rugenstein, J. Bloch-Johnson, J. Wang, D. McInerney, E. J. Moyer, Changes in future precipitation mean and variability across scales. J. Clim. 34, 2741–2758 (2021).
12
A. A. Akinsanola, W. Zhou, T. Zhou, N. Keenlyside, Amplification of synoptic to annual variability of West African summer monsoon rainfall under global warming. NPJ Clim. Atmos. Sci. 3, 21 (2020).
13
L. M. Alves, R. Chadwick, A. Moise, J. Brown, J. A. Marengo, Assessment of rainfall variability and future change in Brazil across multiple timescales. Int. J. Climatol. 41 (suppl. 1), E1875–E1888 (2021).
14
C. Chou, J. Chiang, C. Lan, C. Chung, Y. Liao, C. Lee, Increase in the range between wet and dry season precipitation. Nat. Geosci. 6, 263–267 (2013).
15
D. L. Ficklin, S. E. Null, J. T. Abatzoglou, K. A. Novick, D. T. Myers, Hydrological intensification will increase the complexity of water resource management. Earths Futur. 10, e2021EF002487 (2022).
16
X. Tan, X. Wu, Z. Huang, J. Fu, X. Tan, S. Deng, Y. Liu, T. Y. Gan, B. Liu, Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions. Nat. Commun. 14, 2796 (2023).
17
R. Allan, Amplified seasonal range in precipitation minus evaporation. Environ. Res. Lett. 18, 094004 (2023).
18
D. K. Ray, J. S. Gerber, G. K. MacDonald, P. C. West, Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).
19
L. A. Gherardi, O. E. Sala, Effect of interannual precipitation variability on dryland productivity: A global synthesis. Glob. Change Biol. 25, 269–276 (2019).
20
M. Kotz, A. Levermann, L. Wenz, The effect of rainfall changes on economic production. Nature 601, 223–227 (2022).
21
I. M. Held, B. J. Soden, Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).
22
G. A. Vecchi, B. J. Soden, Global warming and the weakening of the tropical circulation. J. Clim. 20, 4316–4340 (2007).
23
S. Zhou, P. Huang, L. Wang, K. Hu, G. Huang, P. Hu, Robust changes in global subtropical circulation under greenhouse warming. Nat. Commun. 15, 96 (2024).
24
Y. G. Ham, J.-H. Kim, S.-K. Min, D. Kim, T. Li, A. Timmermann, M. F. Stuecker, Anthropogenic fingerprints in daily precipitation revealed by deep learning. Nature 622, 301–307 (2023).
25
A. A. Tsonis, Widespread increases in low-frequency variability of precipitation over the past century. Nature 382, 700–702 (1996).
26
M. J. Menne, I. Durre, R. S. Vose, B. E. Gleason, T. G. Houston, An overview of the Global Historical Climatology Network-Daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).
27
D. Chen, M. Rojas, B. H. Samset, K. Cobb, A. Diongue Niang, P. Edwards, S. Emori, S. H. Faria, E. Hawkins, P. Hope, P. Huybrechts, M. Meinshausen, S. K. Mustafa, G.-K. Plattner, A.-M. Tréguie, “Framing, context, and methods” in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, B. Zhou, Eds. (Cambridge Univ. Press, 2021), pp. 147–286.
28
C. Chou, J. D. Neelin, C. A. Chen, J.-Y. Tu, Evaluating the “rich-get-richer” mechanism in tropical precipitation change under global warming. J. Clim. 22, 1982–2005 (2009).
29
S. Emori, S. J. Brown, Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett. 32, L17706 (2005).
30
H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, A. Simmons, C. Soci, S. Abdalla, X. Abellan, G. Balsamo, P. Bechtold, G. Biavati, J. Bidlot, M. Bonavita, G. De Chiara, P. Dahlgren, D. Dee, M. Diamantakis, R. Dragani, J. Flemming, R. Forbes, M. Fuentes, A. Geer, L. Haimberger, S. Healy, R. J. Hogan, E. Hólm, M. Janisková, S. Keeley, P. Laloyaux, P. Lopez, C. Lupu, G. Radnoti, P. de Rosnay, I. Rozum, F. Vamborg, S. Villaume, J.-N. Thépaut, The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
31
T. Shepherd, Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).
32
C. Deser, A. Phillips, V. Bourdette, H. Y. Teng, Uncertainty in climate change projections: The role of internal variability. Clim. Dyn. 38, 527–546 (2012).
33
K. M. Grise, S. M. Davis, I. R. Simpson, D. W. Waugh, Q. Fu, R. J. Allen, K. H. Rosenlof, C. C. Ummenhofer, K. B. Karnauskas, A. C. Maycock, X.-W. Quan, T. Birner, P. W. Staten, Recent tropical expansion: Natural variability or forced response? J. Clim. 32, 1551–1571 (2019).
34
R. Chemke, L. M. Polvani, Opposite tropical circulation trends in climate models and in reanalyses. Nat. Geosci. 12, 528–532 (2019).
35
R. Chemke, J. Yuval, Human-induced weakening of the Northern Hemisphere tropical circulation. Nature 617, 529–532 (2023).
36
C. M. Wainwright, R. P. Allan, E. Black, Consistent trends in dry spell length in recent observations and future projections. Geophys. Res. Lett. 49, e2021GL097231 (2022).
37
L. Gimeno-Sotelo, R. Sorí, R. Nieto, S. Vicente-Serrano, L. Gimeno, Unravelling the origin of the atmospheric moisture deficit that leads to droughts. Nat. Water 2, 242–253 (2024).
38
N. P. Gillett, H. Shiogama, B. Funke, G. Hegerl, R. Knutti, K. Matthes, B. D. Santer, D. Stone, C. Tebaldi, The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev. 9, 3685–3697 (2016).
39
M. R. Allen, S. F. B. Tett, Checking for model consistency in optimal fingerprinting. Clim. Dyn. 15, 419–434 (1999).
40
M. R. Allen, P. A. Stott, Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Clim. Dyn. 21, 477–491 (2003).
41
A. Ribes, S. Planton, L. Terray, Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Clim. Dyn. 41, 2817–2836 (2013).
42
C. Deser, A. S. Phillips, I. R. Simpson, N. Rosenbloom, D. Coleman, F. Lehner, A. G. Pendergrass, P. DiNezio, S. Stevenson, Isolating the evolving contributions of anthropogenic aerosols and greenhouse gases: A new CESM1 large ensemble community resource. J. Clim. 33, 7835–7858 (2020).
43
J. Quaas, H. Jia, C. Smith, A. L. Albright, W. Aas, N. Bellouin, O. Boucher, M. Doutriaux-Boucher, P. M. Forster, D. Grosvenor, S. Jenkins, Z. Klimont, N. G. Loeb, X. Ma, V. Naik, F. Paulot, P. Stier, M. Wild, G. Myhre, M. Schulz, Robust evidence for reversal of the trend in aerosol effective climate forcing. Atmos. Chem. Phys. 22, 12221–12239 (2022).
44
P. Wu, N. Christidis, P. Stott, Anthropogenic impact on Earth’s hydrological cycle. Nat. Clim. Chang. 3, 807–810 (2013).
45
I. R. Simpson, K. A. McKinnon, D. Kennedy, D. M. Lawrence, F. Lehner, R. Seager, Observed humidity trends in dry regions contradict climate models. Proc. Natl. Acad. Sci. U.S.A. 121, e2302480120 (2024).
46
G. Myhre, K. Alterskjær, C. W. Stjern, Ø. Hodnebrog, L. Marelle, B. H. Samset, J. Sillmann, N. Schaller, E. Fischer, M. Schulz, A. Stohl, Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 9, 16063 (2019).
47
X. Yuan, Y. Wang, P. Ji, P. Wu, J. Sheffield, J. A. Otkin, A global transition to flash droughts under climate change. Science 380, 187–191 (2023).
48
Y. Qing, S. Wang, Z. Yang, P. Gentine, Soil moisture−atmosphere feedbacks have triggered the shifts from drought to pluvial conditions since 1980. Commun. Earth Environ. 4, 254 (2023).
49
W. Zhang, Code for Paper “Anthropogenic amplification of precipitation variability over the past century”, Zenodo (2024); https://doi.org/10.5281/zenodo.11531548.
50
S. Contractor, M. G. Donat, L. V. Alexander, M. Ziese, A. Meyer-Christoffer, U. Schneider, E. Rustemeier, A. Becker, I. Durre, R. S. Vose, Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016. Hydrol. Earth Syst. Sci. 24, 919–943 (2020).
51
M. Chen, W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, J. E. Janowiak, Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res. 113, D04110 (2008).
52
U. Schneider, A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, B. Rudolf, GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2014).
53
H. E. Beck, E. F. Wood, M. Pan, C. K. Fisher, D. G. Miralles, A. I. J. M. van Dijk, T. R. McVicar, R. F. Adler, MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100, 473–500 (2019).
54
C. Funk, P. Peterson, M. Landsfeld, D. Pedreros, J. Verdin, S. Shukla, G. Husak, J. Rowland, L. Harrison, A. Hoell, J. Michaelsen, The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
55
D. Jones, W. Wang, R. Fawcett, High-quality spatial climate data-sets for Australia. Aust. Meteorol. Oceanogr. J. 58, 233–248 (2009).
56
R. Cornes, G. van der Schrier, E. J. M. van den Besselaar, P. D. Jones, An ensemble version of the E-OBS temperature and precipitation datasets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
57
J. Wu, X. Gao, A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys. 56, 1102–1111 (2013).
58
A. Yatagai, K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, A. Kitoh, APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Am. Meteorol. Soc. 93, 1401–1415 (2012).
59
X. Zhang, F. W. Zwiers, P. A. Stott, Multimodel multisignal climate change detection at regional scale. J. Clim. 19, 4294–4307 (2006).
60
H. Wen, X. Zhang, Y. Xu, B. Wang, Detecting human influence on extreme temperatures in China. Geophys. Res. Lett. 40, 1171–1176 (2013).
61
D. Polson, G. C. Hegerl, R. P. Allan, B. B. Sarojini, Have greenhouse gases intensified the contrast between wet and dry regions? Geophys. Res. Lett. 40, 4783–4787 (2013).
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Volume 385 | Issue 6707
26 July 2024
26 July 2024
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Received: 3 March 2024
Accepted: 17 June 2024
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Acknowledgments
Funding: The study is jointly supported by the National Natural Science Foundation of China (41988101), the National Key Research and Development Program of China (2023YFF0805202, 2020YFA0608904), the National Natural Science Foundation of China (42275038), China Meteorological Administration Climate Change Special Program (QBZ202306), and the UK–China Research Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China.
Author contributions: Conceptualization: W.Z. and T.Z. Methodology and investigation: W.Z. Critical insights: P.W. and T.Z. Writing – original draft: W.Z. Writing – review & editing: W.Z., T.Z., and P.W.
Competing interests: The authors declare that they have no competing interests.
Data and materials availability: Daily precipitation observations can be acquired as follows: GHCN-Daily, https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily; REGEN-LONG, https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f6973_9398_8796_3040; CPC_Global, https://climatedataguide.ucar.edu/climate-data/cpc-unified-gauge-based-analysis-global-daily-precipitation; GPCC, https://www.dwd.de/EN/ourservices/gpcc/gpcc.html; MSWEP, https://www.gloh2o.org/mswep/; CHIRPS, https://climatedataguide.ucar.edu/climate-data/chirps-climate-hazards-infrared-precipitation-station-data-version-2; AWAP, https://eo-data.csiro.au/projects/awap/; E-OBS, https://www.ecad.eu/download/ensembles/download.php; CPC_CONUS, https://psl.noaa.gov/data/gridded/data.unified.daily.conus.html; CN05.1, https://ccrc.iap.ac.cn/resource/detail?id=228; and APHRO_MA, APHRO_ME, and APHRO_RU, https://www.chikyu.ac.jp/precip/english/downloads.html. ERA5 reanalysis data can be acquired from https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. CMIP6 model simulations are available at https://esgf-node.llnl.gov/search/cmip6/. The codes used in this paper are available in Zenodo (49).
License information: Copyright © 2024 the authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original US government works. https://www.science.org/about/science-licenses-journal-article-reuse
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National Key Research and Development Program of China: 2020YFA0608904
National Key Research and Development Program of China: 2023YFF0805202
China Meteorological Administration Climate Change Special Program: QBZ202306
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Fig. 1. Observed trend in daily precipitation variability from 1900 to 2020.
Trend in day-to-day precipitation variability according to GHCN-Daily. Linear trend is first computed for stations with long data records, then gridded to 1° by 1° boxes by taking the median of stational trends within each grid box (materials and methods). The trend is normalized with respect to the entire period for each station and expressed in percentage per 10 years. Blue boxes mark the subregions focused on in this study.
Fig. 2. Regional change in daily precipitation variability from 1900 to 2020.
Time series of precipitation variability in multiple observational datasets (details in table S1) for Europe (A), Australia (B), and eastern North America (C). Regional mean precipitation variability is normalized with respect to the entire period for each dataset and expressed as percentage anomaly. See blue boxes in Fig. 1 for region definitions.
Fig. 3. Physical processes for changes in precipitation variability.
Time series of precipitation variability (σP; gray), the variability of vertical moisture advection [
Fig. 4. Anthropogenic influences on precipitation variability change.
(A to C) Linear trend of precipitation variability from 1900 to 2020 under all external forcings [ALL, (A)], greenhouse gas forcing [GHG, (B)], and anthropogenic aerosol forcing [AA, (C)]. Shadings show the multimodel ensemble mean trend, and dots indicate agreement of 10 out of 12 models on the sign of change. (D to F) Regional mean trend of precipitation variability under ALL, natural (NAT), GHG, and AA forcings, for Europe from 1932 to 2020 (D), Australia from 1901 to 2019 (E), and eastern North America from 1900 to 2020 (F). Box-whiskers show the 5th, 25th, 75th, and 95th of the multirun ensemble, and the white horizontal lines show the multimodel ensemble mean. Dashed gray lines denote observed trends from different observational datasets. Note that the periods analyzed for each region are determined by availability of observational records. Trends are normalized with respect to the entire period for each region and expressed as percentage per decade. (G to I) Scaling factors for 1-signal and 3-signal detection analyses for Europe (G), Australia (H), and eastern North America (I). In the detection analyses, the average of multiple observations with full time span is used in the regression model (i.e., GHCN-Daily and E-OBS for Europe, GHCN-Daily and AWAP for Australia, and GHCN-Daily for eastern North America). Dark bars indicate the best estimate of scaling factors and the 5 to 95% confidence intervals, with light bars indicating the confidence intervals with modeled noise doubled.
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References
References
References and Notes
1
K. E. Trenberth, Conceptual framework for changes of extremes of the hydrological cycle with climate change. Clim. Change 42, 327–339 (1999).
2
R. P. Allan, B. J. Soden, Atmospheric warming and the amplification of precipitation extremes. Science 321, 1481–1484 (2008).
3
P. A. O’Gorman, T. Schneider, The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. U.S.A. 106, 14773–14777 (2009).
4
E. Fischer, R. Knutti, Observed heavy precipitation increase confirms theory and early models. Nat. Clim. Chang. 6, 986–991 (2016).
5
H. Douville, K. Raghavan, J. Renwick, R. P. Allan, P. A. Arias, M. Barlow, R. Cerezo-Mota, A. Cherchi, T. Y. Gan, J. Gergis, D. Jiang, A. Khan, W. Pokam Mba, D. Rosenfeld, J. Tierney, O. Zoli, “Water cycle changes” in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, B. Zhou, Eds. (Cambridge Univ. Press, 2021), pp. 1055–1210.
6
A. G. Pendergrass, R. Knutti, F. Lehner, C. Deser, B. M. Sanderson, Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).
7
J. R. Brown, A. F. Moise, R. A. Colman, Projected increases in daily to decadal variability of Asian-Australian monsoon rainfall. Geophys. Res. Lett. 44, 5683–5690 (2017).
8
W. Zhang, K. Furtado, P. Wu, T. Zhou, R. Chadwick, C. Marzin, J. Rostron, D. Sexton, Increasing precipitation variability on daily-to-multiyear time scales in a warmer world. Sci. Adv. 7, eabf8021 (2021).
9
C. He, T. Li, Does global warming amplify interannual climate variability? Clim. Dyn. 52, 2667–2684 (2019).
10
R. R. Wood, F. Lehner, A. G. Pendergrass, S. Schlunegger, Changes in precipitation variability across time scales in multiple global climate model large ensembles. Environ. Res. Lett. 16, 084022 (2021).
11
K. Schwarzwald, A. Poppick, M. Rugenstein, J. Bloch-Johnson, J. Wang, D. McInerney, E. J. Moyer, Changes in future precipitation mean and variability across scales. J. Clim. 34, 2741–2758 (2021).
12
A. A. Akinsanola, W. Zhou, T. Zhou, N. Keenlyside, Amplification of synoptic to annual variability of West African summer monsoon rainfall under global warming. NPJ Clim. Atmos. Sci. 3, 21 (2020).
13
L. M. Alves, R. Chadwick, A. Moise, J. Brown, J. A. Marengo, Assessment of rainfall variability and future change in Brazil across multiple timescales. Int. J. Climatol. 41 (suppl. 1), E1875–E1888 (2021).
14
C. Chou, J. Chiang, C. Lan, C. Chung, Y. Liao, C. Lee, Increase in the range between wet and dry season precipitation. Nat. Geosci. 6, 263–267 (2013).
15
D. L. Ficklin, S. E. Null, J. T. Abatzoglou, K. A. Novick, D. T. Myers, Hydrological intensification will increase the complexity of water resource management. Earths Futur. 10, e2021EF002487 (2022).
16
X. Tan, X. Wu, Z. Huang, J. Fu, X. Tan, S. Deng, Y. Liu, T. Y. Gan, B. Liu, Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions. Nat. Commun. 14, 2796 (2023).
17
R. Allan, Amplified seasonal range in precipitation minus evaporation. Environ. Res. Lett. 18, 094004 (2023).
18
D. K. Ray, J. S. Gerber, G. K. MacDonald, P. C. West, Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).
19
L. A. Gherardi, O. E. Sala, Effect of interannual precipitation variability on dryland productivity: A global synthesis. Glob. Change Biol. 25, 269–276 (2019).
20
M. Kotz, A. Levermann, L. Wenz, The effect of rainfall changes on economic production. Nature 601, 223–227 (2022).
21
I. M. Held, B. J. Soden, Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).
22
G. A. Vecchi, B. J. Soden, Global warming and the weakening of the tropical circulation. J. Clim. 20, 4316–4340 (2007).
23
S. Zhou, P. Huang, L. Wang, K. Hu, G. Huang, P. Hu, Robust changes in global subtropical circulation under greenhouse warming. Nat. Commun. 15, 96 (2024).
24
Y. G. Ham, J.-H. Kim, S.-K. Min, D. Kim, T. Li, A. Timmermann, M. F. Stuecker, Anthropogenic fingerprints in daily precipitation revealed by deep learning. Nature 622, 301–307 (2023).
25
A. A. Tsonis, Widespread increases in low-frequency variability of precipitation over the past century. Nature 382, 700–702 (1996).
26
M. J. Menne, I. Durre, R. S. Vose, B. E. Gleason, T. G. Houston, An overview of the Global Historical Climatology Network-Daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).
27
D. Chen, M. Rojas, B. H. Samset, K. Cobb, A. Diongue Niang, P. Edwards, S. Emori, S. H. Faria, E. Hawkins, P. Hope, P. Huybrechts, M. Meinshausen, S. K. Mustafa, G.-K. Plattner, A.-M. Tréguie, “Framing, context, and methods” in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, B. Zhou, Eds. (Cambridge Univ. Press, 2021), pp. 147–286.
28
C. Chou, J. D. Neelin, C. A. Chen, J.-Y. Tu, Evaluating the “rich-get-richer” mechanism in tropical precipitation change under global warming. J. Clim. 22, 1982–2005 (2009).
29
S. Emori, S. J. Brown, Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett. 32, L17706 (2005).
30
H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, A. Simmons, C. Soci, S. Abdalla, X. Abellan, G. Balsamo, P. Bechtold, G. Biavati, J. Bidlot, M. Bonavita, G. De Chiara, P. Dahlgren, D. Dee, M. Diamantakis, R. Dragani, J. Flemming, R. Forbes, M. Fuentes, A. Geer, L. Haimberger, S. Healy, R. J. Hogan, E. Hólm, M. Janisková, S. Keeley, P. Laloyaux, P. Lopez, C. Lupu, G. Radnoti, P. de Rosnay, I. Rozum, F. Vamborg, S. Villaume, J.-N. Thépaut, The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
31
T. Shepherd, Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).
32
C. Deser, A. Phillips, V. Bourdette, H. Y. Teng, Uncertainty in climate change projections: The role of internal variability. Clim. Dyn. 38, 527–546 (2012).
33
K. M. Grise, S. M. Davis, I. R. Simpson, D. W. Waugh, Q. Fu, R. J. Allen, K. H. Rosenlof, C. C. Ummenhofer, K. B. Karnauskas, A. C. Maycock, X.-W. Quan, T. Birner, P. W. Staten, Recent tropical expansion: Natural variability or forced response? J. Clim. 32, 1551–1571 (2019).
34
R. Chemke, L. M. Polvani, Opposite tropical circulation trends in climate models and in reanalyses. Nat. Geosci. 12, 528–532 (2019).
35
R. Chemke, J. Yuval, Human-induced weakening of the Northern Hemisphere tropical circulation. Nature 617, 529–532 (2023).
36
C. M. Wainwright, R. P. Allan, E. Black, Consistent trends in dry spell length in recent observations and future projections. Geophys. Res. Lett. 49, e2021GL097231 (2022).
37
L. Gimeno-Sotelo, R. Sorí, R. Nieto, S. Vicente-Serrano, L. Gimeno, Unravelling the origin of the atmospheric moisture deficit that leads to droughts. Nat. Water 2, 242–253 (2024).
38
N. P. Gillett, H. Shiogama, B. Funke, G. Hegerl, R. Knutti, K. Matthes, B. D. Santer, D. Stone, C. Tebaldi, The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev. 9, 3685–3697 (2016).
39
M. R. Allen, S. F. B. Tett, Checking for model consistency in optimal fingerprinting. Clim. Dyn. 15, 419–434 (1999).
40
M. R. Allen, P. A. Stott, Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Clim. Dyn. 21, 477–491 (2003).
41
A. Ribes, S. Planton, L. Terray, Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Clim. Dyn. 41, 2817–2836 (2013).
42
C. Deser, A. S. Phillips, I. R. Simpson, N. Rosenbloom, D. Coleman, F. Lehner, A. G. Pendergrass, P. DiNezio, S. Stevenson, Isolating the evolving contributions of anthropogenic aerosols and greenhouse gases: A new CESM1 large ensemble community resource. J. Clim. 33, 7835–7858 (2020).
43
J. Quaas, H. Jia, C. Smith, A. L. Albright, W. Aas, N. Bellouin, O. Boucher, M. Doutriaux-Boucher, P. M. Forster, D. Grosvenor, S. Jenkins, Z. Klimont, N. G. Loeb, X. Ma, V. Naik, F. Paulot, P. Stier, M. Wild, G. Myhre, M. Schulz, Robust evidence for reversal of the trend in aerosol effective climate forcing. Atmos. Chem. Phys. 22, 12221–12239 (2022).
44
P. Wu, N. Christidis, P. Stott, Anthropogenic impact on Earth’s hydrological cycle. Nat. Clim. Chang. 3, 807–810 (2013).
45
I. R. Simpson, K. A. McKinnon, D. Kennedy, D. M. Lawrence, F. Lehner, R. Seager, Observed humidity trends in dry regions contradict climate models. Proc. Natl. Acad. Sci. U.S.A. 121, e2302480120 (2024).
46
G. Myhre, K. Alterskjær, C. W. Stjern, Ø. Hodnebrog, L. Marelle, B. H. Samset, J. Sillmann, N. Schaller, E. Fischer, M. Schulz, A. Stohl, Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 9, 16063 (2019).
47
X. Yuan, Y. Wang, P. Ji, P. Wu, J. Sheffield, J. A. Otkin, A global transition to flash droughts under climate change. Science 380, 187–191 (2023).
48
Y. Qing, S. Wang, Z. Yang, P. Gentine, Soil moisture−atmosphere feedbacks have triggered the shifts from drought to pluvial conditions since 1980. Commun. Earth Environ. 4, 254 (2023).
49
W. Zhang, Code for Paper “Anthropogenic amplification of precipitation variability over the past century”, Zenodo (2024); https://doi.org/10.5281/zenodo.11531548.
50
S. Contractor, M. G. Donat, L. V. Alexander, M. Ziese, A. Meyer-Christoffer, U. Schneider, E. Rustemeier, A. Becker, I. Durre, R. S. Vose, Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016. Hydrol. Earth Syst. Sci. 24, 919–943 (2020).
51
M. Chen, W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, J. E. Janowiak, Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res. 113, D04110 (2008).
52
U. Schneider, A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, B. Rudolf, GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2014).
53
H. E. Beck, E. F. Wood, M. Pan, C. K. Fisher, D. G. Miralles, A. I. J. M. van Dijk, T. R. McVicar, R. F. Adler, MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100, 473–500 (2019).
54
C. Funk, P. Peterson, M. Landsfeld, D. Pedreros, J. Verdin, S. Shukla, G. Husak, J. Rowland, L. Harrison, A. Hoell, J. Michaelsen, The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
55
D. Jones, W. Wang, R. Fawcett, High-quality spatial climate data-sets for Australia. Aust. Meteorol. Oceanogr. J. 58, 233–248 (2009).
56
R. Cornes, G. van der Schrier, E. J. M. van den Besselaar, P. D. Jones, An ensemble version of the E-OBS temperature and precipitation datasets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
57
J. Wu, X. Gao, A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys. 56, 1102–1111 (2013).
58
A. Yatagai, K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, A. Kitoh, APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Am. Meteorol. Soc. 93, 1401–1415 (2012).
59
X. Zhang, F. W. Zwiers, P. A. Stott, Multimodel multisignal climate change detection at regional scale. J. Clim. 19, 4294–4307 (2006).
60
H. Wen, X. Zhang, Y. Xu, B. Wang, Detecting human influence on extreme temperatures in China. Geophys. Res. Lett. 40, 1171–1176 (2013).
61
D. Polson, G. C. Hegerl, R. P. Allan, B. B. Sarojini, Have greenhouse gases intensified the contrast between wet and dry regions? Geophys. Res. Lett. 40, 4783–4787 (2013).
