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

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Month	HTML	PDF	XML	Total
Aug 2025	4,026	223	65	4,314
Sep 2025	8,233	147	54	8,434
Oct 2025	455	70	5	530
Nov 2025	200	285	55	540
Dec 2025	281	250	40	571
Jan 2026	223	245	15	483
Feb 2026	199	229	10	438
Mar 2026	229	233	12	474
Apr 2026	67	51	3	121
May 2026	41	22	1	64
Jun 2026	4	6	2	12
Jul 2026	131	48	10	189
Aug 2026	132	39	7	178
Sep 2026	298	106	90	494
Oct 2026	78	46	31	155

Cumulative views and downloads (calculated since 04 Aug 2025)

Month	HTML	PDF	XML	Total
Aug 2025	4,026	223	65	4,314
Sep 2025	8,233	147	54	8,434
Oct 2025	455	70	5	530
Nov 2025	200	285	55	540
Dec 2025	281	250	40	571
Jan 2026	223	245	15	483
Feb 2026	199	229	10	438
Mar 2026	229	233	12	474
Apr 2026	67	51	3	121
May 2026	41	22	1	64
Jun 2026	4	6	2	12
Jul 2026	131	48	10	189
Aug 2026	132	39	7	178
Sep 2026	298	106	90	494
Oct 2026	78	46	31	155

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Total article views: 16,997 (including HTML, PDF, and XML) Thereof 16,764 with geography defined and 233 with unknown origin.

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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.