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Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO


Distortion of the water cycle, particularly of its extremes (droughts and pluvials), will be among the most conspicuous consequences of climate change. Here we applied a novel approach with terrestrial water storage observations from the GRACE and GRACE-FO satellites to delineate and characterize 1,056 extreme events during 2002–2021. Dwarfing all other events was an ongoing pluvial that began in 2019 and engulfed central Africa. Total intensity of extreme events was strongly correlated with global mean temperature, more so than with the El Niño Southern Oscillation or other climate indicators, suggesting that continued warming of the planet will cause more frequent, more severe, longer and/or larger droughts and pluvials. In three regions, including a vast swath extending from southern Europe to south-western China, the ratio of wet to dry extreme events decreased substantially over the study period, while the opposite was true in two regions, including sub-Saharan Africa from 5° N to 20° N.

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Fig. 1: The most intense wet and dry events.
Fig. 2: Relationships between extreme events, ENSO and global surface temperature.
Fig. 3: Correlations between extreme event total intensity and global mean temperature by climate zone.
Fig. 4: Regional coherence of extreme event timing.

Data availability

The GRACE/FO products (CSR GRACE/GRACE-FO RL06 Mascon Solutions, version 02) used in our analyses are available from the University of Texas CSR ( The output from a global GRACE/FO data assimilating instance of the CLSM (GRACEDADM_CLSM025GL_7D 3.0) used to fill the 11 month gap between the GRACE and GRACE-FO missions and 18 additional missing months is available from the Goddard Earth Sciences Data and Information Services Center ( The climate oscillation indicator data can be downloaded from the NOAA Physical Sciences Laboratory ( and The global mean temperature data are available from the NASA Goddard Institute for Space Studies ( Köppen-Geiger climate map data are available for download at Key data66 including those used to create the four main text figures are available at

Code availability

The Python code for the ST-DBSCAN clustering algorithm was obtained from the Github repository ( Statistical analyses were performed and figures were generated using NCL software.


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This study was funded by NASA’s GRACE-FO Science Team and NASA’s Energy and Water Cycle Study (NEWS) programme. Computing resources supporting this work were provided by NASA’s High-End Computing (HEC) programme through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. GRACE and GRACE-FO were jointly developed and operated by NASA, DLR and the GFZ German Research Centre for Geosciences.

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Authors and Affiliations



M.R. designed the study with input from B.L. B.L. led the clustering, correlation and uncertainty analyses with input from M.R. M.R. designed the figures, and B.L. created them. M.R. and B.L. discussed the results and wrote the paper.

Corresponding author

Correspondence to Matthew Rodell.

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The authors declare no competing interests.

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Nature Water thanks Melissa Rohde and Soumendra Bhanja for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Locations of the top 30 wet events.

Blue shading represents the portion of an event period during which a grid cell experienced wet conditions (> 1σ, location-specific). Intensity and time period (in paratheses) are noted at the top of each panel.

Extended Data Fig. 2 Locations of the top 30 dry events.

Red shading represents the portion of an event period during which a grid cell experienced dry conditions (< −1σ, location-specific). Intensity and time period (in paratheses) are noted at the top of each panel.

Extended Data Fig. 3 Changing frequency of events in the regions of coherence.

The number of wet (left column) and dry (right column) events active in each year in the five polygons shown in Fig. 4.

Extended Data Table 1 Relationships between event metrics and global mean temperature
Extended Data Table 2 Mean statistics of extreme wet events by climate zone
Extended Data Table 3 Mean statistics of extreme dry events by climate zone

Source data

Source Data Fig. 1

Data used to create the 14 inset time series plots in Fig. 1. Note the map data are also available at as and

Source Data Fig. 2

Time series data used to create Fig. 2.

Source Data Fig. 3

Time series data used to create Fig. 3b,c. Note that Köppen–Geiger climate map data (Fig. 3a) are available for download at

Source Data Fig. 4

Location, year and intensity data used to create the maps in Fig. 4.

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Rodell, M., Li, B. Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO. Nat Water 1, 241–248 (2023).

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