Future projections of global mean precipitation change (ΔP) based on Earth-system models have larger uncertainties than projections of global mean temperature changes (ΔT)1. Although many observational constraints on ΔT have been proposed, constraints on ΔP have not been well studied2,3,4,5 and are often complicated by the large influence of aerosols on precipitation4. Here we show that the upper bound (95th percentile) of ΔP (2051–2100 minus 1851–1900, percentage of the 1980–2014 mean) is lowered from 6.2 per cent to 5.2–5.7 per cent (minimum–maximum range of sensitivity analyses) under a medium greenhouse gas concentration scenario. Our results come from the Coupled Model Intercomparison Project phase 5 and phase 6 ensembles6,7,8, in which ΔP for 2051–2100 is well correlated with the global mean temperature trends during recent decades after 1980 when global anthropogenic aerosol emissions were nearly constant. ΔP is also significantly correlated with the recent past trends in precipitation when we exclude the tropical land areas with few rain-gauge observations. On the basis of these significant correlations and observed trends, the variance of ΔP is reduced by 8–30 per cent. The observationally constrained ranges of ΔP should provide further reliable information for impact assessments.
Your institute does not have access to this article
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
All data that support the findings of this study are available from the following: CMIP5, https://esgf-node.llnl.gov/search/cmip5/ (last access, 9 February 2021); CMIP6, https://esgf-node.llnl.gov/search/cmip6/ (last access, 9 February 2021); HadCRUT4, https://www.metoffice.gov.uk/hadobs/hadcrut4/ (last access, 7 October 2020); GISTEMP4, https://data.giss.nasa.gov/gistemp/ (last access, 9 March 2020); MSWEP2 (v2.2), http://www.gloh2o.org/ (last access, 30 September 2020); GSWP3, http://search.diasjp.net/en/dataset/GSWP3_EXP1_Forcing (last access, 13 October 2020); GPCC, https://www.dwd.de/EN/ourservices/gpcc/gpcc.html (last access, 26 February 2021).
The codes are available from https://doi.org/10.6084/m9.figshare.16816714.
Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (Cambridge Univ. Press, 2013).
Hall, A. et al. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).
Brient, F. Reducing uncertainties in climate projections with emergent constraints: concepts, examples and prospects. Adv. Atmos. Sci. 37, 1–15 (2020).
Allen, M. & Ingram, W. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 228–232 (2002).
Schlund, M. et al. Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? Earth Syst. Dyn. 11, 1233–1258 (2020).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122, 387–400 (2014).
Knutti, R. The end of model democracy? Climatic Change 102, 395–404 (2010).
Shiogama, H. et al. Observational constraints indicate risk of drying in the Amazon Basin. Nat. Commun. 2, 253 (2011).
Caldwell, P. M. et al. Statistical significance of climate sensitivity predictors obtained by data mining. Geophys. Res. Lett. 41, 1803–1808 (2014).
Samset, B. H. et al. Fast and slow precipitation responses to individual climate forcers: a PDRMIP multimodel study. Geophys. Res. Lett. 43, 2782–2791 (2016).
Thorpe, L. & Andrews, T. The physical drivers of historical and 21st century global precipitation changes. Environ. Res. Lett. 9, 064024 (2014).
Salzmann, M. Global warming without global mean precipitation increase? Sci. Adv. 2, e1501572 (2016).
Wu, P., Christidis, N. & Stott, P. Anthropogenic impact on Earth’s hydrological cycle. Nat. Clim. Change 3, 807–810 (2013).
Rao, S. et al. Future air pollution in the shared socio-economic pathways. Glob. Environ. Change 42, 346–358 (2017).
Lund, M. T., Myhre, G. & Samset, B. H. Anthropogenic aerosol forcing under the shared socioeconomic pathways. Atmos. Chem. Phys. 19, 13827–13839 (2019).
Fläschner, D., Mauritsen, T. & Stevens, B. Understanding the intermodel spread in global-mean hydrological sensitivity. J. Clim. 29, 801–817 (2016).
DeAngelis, A. M., Qu, X., Zelinka, M. D. & Hall, A. An observational radiative constraint on hydrologic cycle intensification. Nature 528, 249–253 (2015).
Watanabe, M. et al. Low clouds link equilibrium climate sensitivity to hydrological sensitivity. Nat. Clim. Change 8, 901–906 (2018).
Pendergrass, A. G. The global-mean precipitation response to CO2-induced warming in CMIP6 models. Geophys. Res. Lett. 47, e2020GL089964 (2020).
Jiménez-de-la-Cuesta, D. & Mauritsen, T. Emergent constraints on Earth’s transient and equilibrium response to doubled CO2 from post-1970s global warming. Nat. Geosci. 12, 902–905 (2019).
Tokarska, K. B. et al. Past warming trend constrains future warming in CMIP6 models. Sci. Adv. 6, eaaz9549 (2020).
Nijsse, F. J. M. M., Cox, P. M. & Williamson, M. S. Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models. Earth Syst. Dyn. 11, 737–750 (2020).
Liang, Y., Gillett, N. P. & Monahan, A. H. Climate model projections of 21st century global warming constrained using the observed warming trend. Geophys. Res. Lett. 47, e2019GL086757 (2020).
Hegerl, G. C. et al. Challenges in quantifying changes in the global water cycle. Bull. Am. Meteorol. Soc. 96, 1097–1115 (2015).
Bowman, K. W., Cressie, N., Qu, X. & Hall, A. A hierarchical statistical framework for emergent constraints: application to snow-albedo feedback. Geophys. Res. Lett. 45, 13050–13059 (2018).
Morice, C. P., Kennedy, J. J., Rayner, N. A. & Jones, P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 dataset. J. Geophys. Res. 117, D08101 (2012).
Lenssen, N. et al. Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos. 124, 6307–6326 (2019).
Gillett, N. P. et al. The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev. 9, 3685–3697 (2016).
Gillett, N. P. et al. Constraining human contributions to observed warming since preindustrial. Nat. Clim. Change 11, 207–212 (2021).
Sun, Q. et al. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2018).
Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn 93, 5–48 (2015).
Adler, R. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).
Beck, H. E. et al. MSWEP V2 global 3‑hourly 0.1° precipitation: methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100, 473–500 (2019).
van den Hurk, B. et al. LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project—aims, setup and expected outcome. Geosci. Model Dev. 9, 2809–2832 (2016).
Becker, A. et al. A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst. Sci. Data 5, 71–99 (2013).
Emori, S. & Brown, S. J. Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett. 32, L17706 (2005).
Xie, P. & Arkin, P. A. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc. 78, 2539–2558 (1997).
Yin, X. A. & Gruber Arkin, P. Comparison of the GPCP and CMAP merged gauge–satellite monthly precipitation products for the period 1979–2001. J. Hydrometeorol. 5, 1207–1222 (2004).
Compo, G. P. et al. The Twentieth Century Reanalysis Project. Q. J. R. Meteorol. Soc. A 137, 1–28 (2011).
Kim, H. Global Soil Wetness Project Phase 3 atmospheric boundary conditions (experiment 1) (DIAS, 2017); https://doi.org/10.20783/DIAS.501
Schneider, U. et al. GPCC Full Data Monthly Product Version 2020 at 1.0°: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historical Data (2020); https://doi.org/10.5676/DWD_GPCC/FD_M_V2020_100
This work was supported by the Integrated Research Program for Advancing Climate Models (JPMXD0717935457), Grants-in-Aid for Scientific Research (JP21H01161) of the Ministry of Education, Culture, Sports, Science and Technology of Japan, the Environment Research and Technology Development Fund (JPMEERF20202002) and the National Research Foundation of Korea grant (MSIT) (2021H1D3A2A03097768).
The authors declare no competing interests.
Peer review information
Nature thanks Chad Thackeray, Sabine Undorf and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Horizontal and vertical axes indicate the future (2051–2100 minus 1851–1900) ΔT (°C) and ΔP (% of the 1980–2014 mean), respectively. Crosses and diamonds are CMIP5 and CMIP6 ESMs (ensemble mean for each ESM), respectively. Pearson’s correlations of the CMIP5 and CMIP6 ESMs are denoted in the panel. Those correlations are significant at the 5% level.
White shaded areas in the top panels indicate tropical (30° S–30° N) land regions where 1980–2014 mean numbers of rain gauge observations37 (Methods) are less than (a) 1, (b) 2 and (c) 3. Panels (d), (e) and (f) show P* anomalies relative to the 1980–2014 mean (%). Here P* represents the precipitation averaged over the pink shading areas of panels (a), (b) and (c), respectively. Solid lines are GPCP34 (red), MSWEP235 (green) and GSWP336 (blue). Dashed lines show their linear trends. Panel (e) is the same as Fig. 2b. We mainly focus on the case of panels (b) and (e) in this paper. (g) Relationships between the 1980–2014 trends of P and P* (P* in the case of (e)). Vertical and horizontal axes indicate the 1980–2014 trends of P and P* (% per 35 yr), respectively. Crosses and diamonds are CMIP56 and CMIP67,8 ESMs (ensemble mean for each ESM), respectively. Dashed line indicates the linear regression. Pearson’s correlation of the CMIP5 and CMIP6 ESMs is denoted in the panel. This correlation is significant at the 5% level. Grads was used to draw the maps.
Horizontal axes show the recent past (1980–2014) trends of (top) T (°C per 35 yr) and (bottom) P* (% per 35 yr) for (left) CMIP5 and (right) CMIP6. Vertical axes indicate the future ΔP (2051–2100 minus 1851–1900 of hist+4.5, % of the 1980–2014 mean values). P* indicates precipitation averaged over the world except for some tropical land regions with few rain gauge observations (Extended Data Fig. 2b). Crosses and diamonds are CMIP5 and CMIP6 ESMs (ensemble mean for each ESM), respectively. Purple crosses/diamonds denote the ESMs whose recent past T trends are higher than the upper bound of HadCRUT4. Pearson’s correlations of the ESMs are denoted in the panels. Those correlations are significant at the 5% level. Dashed lines show the linear regressions. Horizontal bars indicate the 5–95% ranges of HadCRUT4 (light blue), GISTEMP4 (light green), GPCP (red), MSWEP2 (green) and GSWP3 (blue) (Methods). Box plots show the average (white line), 17–83% range (box), and 5–95% range (vertical bar) for the raw ESMs (black) and the constrained ranges using the observations (colours; navy and yellow for Had+GIS and GP+MS+GS, respectively).
(a) Long-term (left, 1851–2014, % per 164 yr) and recent (right, 1980–2014, % per 35 yr) past trends of P in the ensembles of hist-GHG (red), hist-aer (blue) and hist-nat (green). (b) Horizontal and vertical axes are the long-term past trends of P (% per 164 yr) in hist+4.5 and hist-GHG, respectively. (c) Horizontal and vertical axes are the recent past trends of P (% per 35 yr) in hist+4.5 and hist-GHG, respectively.
Vertical axis indicates the future ΔP (2051–2100 minus 1851–1900 of hist+4.5, % of the 1980–2014 mean values). Horizontal axis shows the recent past (1980–2014) trends of P (% per 35 yr). Crosses and diamonds are CMIP5 and CMIP6 ESMs (ensemble mean for each ESM), respectively. Purple crosses/diamonds denote the ESMs whose recent past T trends are higher than the upper bound of HadCRUT4. Dashed line shows the linear regression. Horizontal bars indicate the 5–95% ranges of GPCP (red), MSWEP2 (green) and GSWP3 (blue) (see Methods). Box plots show the average (white line), 17–83% range (box), and 5–95% range (vertical bar) for the raw CMIP5 and CMIP6 ESMs (black) and the constrained ranges using the observations (colours; yellow for GP+MS+GS). Triangle and asterisk symbols denote the 5–95% ranges using only the CMIP5 or CMIP6 ESMs, respectively. Pearson’s correlations of the CMIP5 and CMIP 6 ESMs are denoted in the panel. Those correlations are significant at the 5% level.
Solid lines indicate the time series of precipitation anomalies relative to the 1980–2014 mean (%) averaged over (a) the ocean area plus Antarctica and (b) the land area except for Antarctica. Dashed lines show the linear trends. Red, green, blue and black (only for (b)) lines are GPCP, MSWEP2, GSWP3 and GPCC, respectively.
Vertical axes indicate the future ΔP (2051–2100 minus 1851–1900 of hist+4.5, % of the 1980–2014 mean values) of the CMIP5 and CMIP6 ESMs. Box plots show the average (white line), 17–83% range (box), and 5–95% range (vertical bar) for the raw CMIP5/6 ESMs (black) and the constrained ranges using the P* trends of GP+MS+GS (yellow). The horizontal axis indicates the thresholds of rain gauge numbers used for the calculation of P*.
Vertical axes indicate the future dP/dT (calculated by dividing ΔP by ΔT of ‘2051–2100 minus 1851–1900’). Horizontal axes show the recent past (a) dP/dT and (b) dP*/dT (calculated by dividing the 1980–2014 trends of P and P* by the 1980–2014 T trends). Pearson’s correlations of the CMIP5 and CMIP6 ESMs are denoted in the panels. Those correlations are significant at the 5% level except for the CMIP5 of (b). Horizontal bars indicate the 5–95% ranges of GPCP (red), MSWEP2 (green) and GSWP3 (blue). Box plots show the average (white line), 17–83% range (box), and 5–95% range (vertical bar) for the raw CMIP5 and CMIP6 ESMs (black) and the constrained ranges using observations (colours). Because all the CMIP5 and CMIP6 ESMs are out of the range of MSWEP2/GISTEMP4 in (a), the corresponding constrained range is not available. Triangle and asterisk symbols denote the 5–95% ranges using only the CMIP5 or CMIP6 ESMs, respectively.
About this article
Cite this article
Shiogama, H., Watanabe, M., Kim, H. et al. Emergent constraints on future precipitation changes. Nature 602, 612–616 (2022). https://doi.org/10.1038/s41586-021-04310-8