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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
29 Jun 2026
Development and technical paper |
| 29 Jun 2026
| 29 Jun 2026
Global climate modeling with improved precipitation characteristics by learning physics (GRIST-MPS v1.0) from global storm-resolving modeling
Yiming Wang
Department of Computer Science and Technology, Tsinghua University, Beijing, China
State Key Laboratory of Climate System Prediction and Risk Management (CPRM)/Key Laboratory of Meteorological Disaster, Ministry of Education/School of Atmospheric Sciences/Institute of Energy Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
Yilun Han
Department of Earth System Science, Tsinghua University, Beijing, China
Scripps Institution of Oceanography, La Jolla, CA, USA
Wei Xue
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Tianru Chen
State Key Laboratory of Climate System Prediction and Risk Management (CPRM)/Key Laboratory of Meteorological Disaster, Ministry of Education/School of Atmospheric Sciences/Institute of Energy Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
Yihui Zhou
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing, China
Xiaohan Li
State Key Laboratory of Climate System Prediction and Risk Management (CPRM)/Key Laboratory of Meteorological Disaster, Ministry of Education/School of Atmospheric Sciences/Institute of Energy Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
State Key Laboratory of Climate System Prediction and Risk Management (CPRM)/Key Laboratory of Meteorological Disaster, Ministry of Education/School of Atmospheric Sciences/Institute of Energy Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
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Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, 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.
This study demonstrates that short-period Global Storm Resolving Model (GSRM) simulations can...
