Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can improve ESM simulations locally but cannot correct errors in modelled spatial patterns. Here we propose a framework based on physically constrained generative adversarial networks to improve local distributions and spatial structure simultaneously. We apply our approach to the computationally efficient CM2Mc–LPJmL ESM. Our method outperforms existing ones in correcting local distributions and leads to strongly improved spatial patterns, especially regarding the intermittency of daily precipitation. Notably, a double-peaked Intertropical Convergence Zone, a common problem in ESMs, is removed. Enforcing a physical constraint to preserve global precipitation sums, the generative adversarial network can generalize to future climate scenarios unseen during training. Feature attribution shows that the generative adversarial network identifies regions where the ESM exhibits strong biases. Our method constitutes a general framework for correcting ESM variables and enables realistic simulations at a fraction of the computational cost.
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The authors thank the referees for their helpful comments and suggestions. N.B. and P.H. acknowledge funding by the Volkswagen Foundation, as well as the European Regional Development Fund (ERDF), the German Federal Ministry of Education and Research and the Land Brandenburg for supporting this project by providing resources on the high-performance computer system at the Potsdam Institute for Climate Impact Research. M.D. acknowledges funding by the Volkswagen Foundation project POEM-PBSim. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting F.M.S. N.B. acknowledges further funding by the Federal Ministry of Education and Research under grant no. 01LS2001A.
The authors declare no competing interests.
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Hess, P., Drüke, M., Petri, S. et al. Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nat Mach Intell 4, 828–839 (2022). https://doi.org/10.1038/s42256-022-00540-1
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