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

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

doi:10.5194/gmd-19-5553-2026

Articles | Volume 19, issue 12

https://doi.org/10.5194/gmd-19-5553-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-5553-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 12

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

Supplement

https://doi.org/10.5194/gmd-19-5553-2026-supplement

Data sets

Global Climate Modeling with Improved Precipitation Characteristics by Learning Physics (GRIST-MPS v1.0) from Global Storm-Resolving Modeling [Data set] GRIST-Dev https://doi.org/10.5281/zenodo.15853268

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.