Articles | Volume 19, issue 12
https://doi.org/10.5194/gmd-19-5553-2026
https://doi.org/10.5194/gmd-19-5553-2026
Development and technical paper
 | 
29 Jun 2026
Development and technical paper |  | 29 Jun 2026

Global climate modeling with improved precipitation characteristics by learning physics (GRIST-MPS v1.0) from global storm-resolving modeling

Yiming Wang, Yi Zhang, Yilun Han, Wei Xue, Tianru Chen, Yihui Zhou, Xiaohan Li, and Haishan Chen

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Short summary

This study demonstrates that short-period Global Storm Resolving Model (GSRM) simulations can inform long-term Global Climate Model (GCM) integrations through a machine-learning-based physics suite. With 80 d of GSRM-derived training data, the hybrid model achieves stable multiyear climate simulations and improved precipitation climatic characteristics.

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