Abstract
Extreme precipitation (EP) is a major climate risk, yet its projections remain uncertain due to the combined influence of thermodynamic (TH) and dynamic (DY) processes. Using multi-model simulations under three emission scenarios, we separate TH and DY contributions to the annual maximum 1-day precipitation (Rx1Day) and quantify their uncertainties. TH consistently intensifies extremes with warming, while DY strongly modulates their magnitude and direction. DY processes dominate Rx1Day uncertainty, with internal variability within DY emerging as the leading contributor. Signal-to-noise ratio (SNR) analysis shows that the forced signal emerges more clearly for TH than DY, where chaotic variability fundamentally limits predictability. The strongest intensification occurs in equatorial regions, raising equity concerns for vulnerable populations. These results demonstrate that DY internal variability is the primary driver of EP uncertainty, highlighting limits to long-term predictability and the importance of properly representing natural dynamical fluctuations in future projections.
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Data availability
The GCM data underpinning this study can be obtained from the CMIP6 data portal: https://esgf-metagrid.cloud.dkrz.de/.
Code availability
The Python code for the analysis and figure generation is available upon reasonable request.
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Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00208210).
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M.S. and K.A. conceived the study. M.S. and K.A. performed the analyses and interpreted the data. M.S. and K.A. led the writing, and D.P. contributed through review and editing. All authors reviewed and approved the final version of the manuscript.
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Sothearith, M., Park, D. & Ahn, KH. Dynamic internal variability dominates uncertainty in modeling future extreme precipitation. npj Clim Atmos Sci (2026). https://doi.org/10.1038/s41612-025-01318-z
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DOI: https://doi.org/10.1038/s41612-025-01318-z
