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Constraining the increased frequency of global precipitation extremes under warming

Nature Climate Change volume 12, pages 441–448 (2022)Cite this article

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Abstract

A key indicator of climate change is the greater frequency and intensity of precipitation extremes across much of the globe. In fact, several studies have already documented increased regional precipitation extremes over recent decades. Future projections of these changes, however, vary widely across climate models. Using two generations of models, here we demonstrate an emergent relationship between the future increased occurrence of precipitation extremes aggregated over the globe and the observable change in their frequency over recent decades. This relationship is robust in constraining frequency changes in precipitation extremes in two separate ensembles and under two future emissions pathways (reducing intermodel spread by 20–40%). Moreover, this relationship is also apparent when the analysis is limited to near-global land regions. These constraints suggest that historical global precipitation extremes will occur roughly 32 ± 8% more often than at present by 2100 under a medium-emissions pathway (and 55 ± 13% more often under high emissions).

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Fig. 1: Simulated future change in precipitation characteristics.
Fig. 2: Historical change in extreme precipitation occurrence.
Fig. 3: Maps of the simulated historical and future change in FP≥99.
Fig. 4: EC on the future change in FP≥99.
Fig. 5: Constraints on FP≥99 under two emissions scenarios.
Fig. 6: Constraints on other characteristics of future extreme precipitation.

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Data availability

The data that support the findings of this study are publicly available. The CMIP5 and CMIP6 output is available from the Earth System Grid Federation (https://esgf-node.llnl.gov/projects/). The observational precipitation data are available from http://www.gloh2o.org/mswep/, https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f8555_9260_4736_9502, https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f6973_9398_8796_3040, https://data.chc.ucsb.edu/products/CHIRPS-2.0/ and https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. Source data are provided with this paper.

Code availability

The code used in the analyses described in this study is available in a GitHub repository: https://github.com/cwthackeray/T22_NCC (ref. 73). More information about the code can be obtained from the corresponding author upon reasonable request.

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Acknowledgements

We acknowledge funding from the National Science Foundation grant no. 1543268, titled ‘Reducing Uncertainty Surrounding Climate Change Using Emergent Constraints’ (C.W.T. and A.H.), and the Regional and Global Model Analysis Program for the Office of Science of the US Department of Energy through the Program for Climate Model Diagnosis and Intercomparison (C.W.T., A.H., J.N. and D.C.). We also thank the World Climate Research Programme’s Working Group on Coupled Modeling and the individual modelling groups for their roles in making CMIP data available. All data used here are publicly available.

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  1. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USA

    Chad W. Thackeray, Alex Hall, Jesse Norris & Di Chen

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  1. Chad W. Thackeray
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Contributions

C.W.T. conceived of the study and designed the analyses. C.W.T. conducted the analyses and wrote the manuscript, while A.H., J.N. and D.C. provided comments and feedback.

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Correspondence to Chad W. Thackeray.

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Nature Climate Change thanks Lu Dong, Nidhi Nishant, Peili Wu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Future simulated change in extreme precipitation in CMIP5 and CMIP6.

(Top) Ensemble mean change in precipitation greater than or equal to the 99th percentile per degree of global warming from CMIP5 and CMIP6 along with the difference between ensembles (CMIP6 minus CMIP5). (Bottom) Standard deviation of extreme precipitation change per degree of global warming in CMIP5 and CMIP6, along with the difference between ensembles.

Extended Data Fig. 2 Relationship between simulated historical warming rates and historical changes in extreme precipitation occurrence.

Scatterplot showing the simulated historical warming trend (K/decade) from each CMIP5 and CMIP6 model along with their respective historical FP≥99 change (%/decade). Dashed lines are derived from ordinary least squares regression.

Extended Data Fig. 3 Same as Fig. 3 but showing the CMIP5 ensemble means rather than that of CMIP6.

Stippling denotes grid cells where (~75% or more) of models agree on the sign of change.

Extended Data Fig. 4 Spatial characteristics of historical extreme precipitation frequency change derived from MSWEP2.

Note that the scale is different from Fig. 3a and ED Fig. 3a. This is because we only have one “realization” for observations whereas the ensemble means from CMIP5 and CMIP6, which have been smoothed across 20+ models. Individual GCMs exhibit magnitudes of change that are more comparable to MSWEP2 (not shown).

Extended Data Fig. 5 Sensitivity of the CMIP6 emergent relationship to the number of realizations used to calculate historical and future FP≥99 change.

(a) Scatterplot of the emergent relationship defined in three different ways: using all available simulations from 23 CMIP6 models as in Figs. 4a and 5a (shown in navy), using only the first realization (r1) from the same model subset (shown in purple), and using only the mean of the frequency change from the first three realizations (r1, r2, r3) from 17 CMIP6 GCMs (shown in light blue). The ensemble size decreases in the latter case because several GCMs only provide one realization. The historical metric is calculated over the 1980–2017 period for all cases. If we were to extend the historical period to 2020 (as was done for CMIP5 in Fig. 4), the r value for the r1 case increases to 0.75. (b) Raw and constrained 95% prediction intervals of future FP≥99 change derived using the Bowman et al. (2018) framework for each of the ways to construct the emergent relationship. The wider portion of each bar denotes the 68% prediction interval.

Extended Data Fig. 6 Impact of future warming trends on emergent relationship slope.

(a) Same as Fig. 4a, but with each GCM colored by their equilibrium climate sensitivity (as provided by Zelinka et al. 2020) rather than their CMIP ensemble. (b) Same as Fig. 4a, but the future change metric (y-axis) is normalized by warming (units of %/K).

Extended Data Fig. 7 Map of land areas considered after masking by REGEN product data quality.

Determined using a threshold for the kriging error term used to interpolate point data to gridded averages (Contractor et al. 2020). This mask is derived using REGEN-LONG data at the mid-point of our observational time period (1980–2016). REGEN-LONG is more restrictive than REGEN-ALL.

Extended Data Fig. 8 Relationship between the future change in extreme precipitation frequency and magnitude.

Scatterplot of simulated changes in frequency (%) and magnitude (mm/day) of extreme precipitation events (≥99th percentile). Only showing the results for RCP8.5 (CMIP5) and SSP5-8.5 (CMIP6) here.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Tables 1–4.

Source data

Source Data Fig. 1

Data for the global hydrological sensitivity box plots shown in Fig. 1a.

Source Data Fig. 2

Time series of the historical frequency of precipitation exceeding the 99th percentile.

Source Data Fig. 4

Model data for EC scatterplots.

Source Data Fig. 5

Model data for EC scatterplots.

Source Data Fig. 6

Model data for EC scatterplots.

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Thackeray, C.W., Hall, A., Norris, J. et al. Constraining the increased frequency of global precipitation extremes under warming. Nat. Clim. Chang. 12, 441–448 (2022). https://doi.org/10.1038/s41558-022-01329-1

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