MSWEP V3: Machine Learning-Powered Global Precipitation Estimates at 0.1$^\circ$ Hourly Resolution (1979-Present)
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918318569422848 |
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| author | Wang, Xuetong Alharbi, Raied S. Baez-Villanueva, Oscar M. Miralles, Diego G. Ma, Jun Xu, Shiqin McCabe, Matthew F. Pappenberger, Florian van Dijk, Albert I. J. M. McVicar, Tim R. Karthikeyan, Lanka Fowler, Hayley J. Pan, Ming Gebrechorkos, Solomon H. Beck, Hylke E. |
| author_facet | Wang, Xuetong Alharbi, Raied S. Baez-Villanueva, Oscar M. Miralles, Diego G. Ma, Jun Xu, Shiqin McCabe, Matthew F. Pappenberger, Florian van Dijk, Albert I. J. M. McVicar, Tim R. Karthikeyan, Lanka Fowler, Hayley J. Pan, Ming Gebrechorkos, Solomon H. Beck, Hylke E. |
| contents | We introduce Version 3 (V3) of the gridded near real-time Multi-Source Weighted-Ensemble Precipitation (MSWEP) product -- the first fully global, historical machine learning powered precipitation (P) dataset, developed to meet the growing demand for timely and accurate P estimates amid escalating climate challenges. MSWEP V3 provides hourly data at 0.1$^\circ$ resolution from 1979 to the present, continuously updated with a latency of approximately two hours. Development follows a two-stage process. First, baseline P fields are generated using machine learning model stacks that integrate satellite- and (re)analysis-based P and air-temperature products, along with static variables. The models are trained using hourly and daily observations from 15,959 P gauges worldwide. Second, these baseline P fields are corrected using daily and monthly gauge observations from 57,666 and 86,000 stations globally. To assess MSWEP V3's baseline performance, we evaluated 19 (quasi-) global gridded P products -- including both uncorrected and gauge-based products -- using observations from an independent set of 15,958 gauges excluded from the first training stage. The MSWEP V3 baseline achieved a median daily Kling-Gupta Efficiency (KGE) of 0.69, outperforming all evaluated products. Other uncorrected products achieved median daily KGE values of 0.61 (ERA5), 0.46 (IMERG-L V7), 0.38 (GSMaP V8), and 0.31 (CHIRP). Using leave-one-out cross-validation, the daily gauge correction was found to improve the median daily correlation by 0.09, constrained by the already strong baseline performance. We anticipate that MSWEP V3 -- accessible at www.gloh2o.org/mswep -- will enable more reliable monitoring, forecasting, and management of water-related risks in a variable and changing climate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01436 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MSWEP V3: Machine Learning-Powered Global Precipitation Estimates at 0.1$^\circ$ Hourly Resolution (1979-Present) Wang, Xuetong Alharbi, Raied S. Baez-Villanueva, Oscar M. Miralles, Diego G. Ma, Jun Xu, Shiqin McCabe, Matthew F. Pappenberger, Florian van Dijk, Albert I. J. M. McVicar, Tim R. Karthikeyan, Lanka Fowler, Hayley J. Pan, Ming Gebrechorkos, Solomon H. Beck, Hylke E. Atmospheric and Oceanic Physics We introduce Version 3 (V3) of the gridded near real-time Multi-Source Weighted-Ensemble Precipitation (MSWEP) product -- the first fully global, historical machine learning powered precipitation (P) dataset, developed to meet the growing demand for timely and accurate P estimates amid escalating climate challenges. MSWEP V3 provides hourly data at 0.1$^\circ$ resolution from 1979 to the present, continuously updated with a latency of approximately two hours. Development follows a two-stage process. First, baseline P fields are generated using machine learning model stacks that integrate satellite- and (re)analysis-based P and air-temperature products, along with static variables. The models are trained using hourly and daily observations from 15,959 P gauges worldwide. Second, these baseline P fields are corrected using daily and monthly gauge observations from 57,666 and 86,000 stations globally. To assess MSWEP V3's baseline performance, we evaluated 19 (quasi-) global gridded P products -- including both uncorrected and gauge-based products -- using observations from an independent set of 15,958 gauges excluded from the first training stage. The MSWEP V3 baseline achieved a median daily Kling-Gupta Efficiency (KGE) of 0.69, outperforming all evaluated products. Other uncorrected products achieved median daily KGE values of 0.61 (ERA5), 0.46 (IMERG-L V7), 0.38 (GSMaP V8), and 0.31 (CHIRP). Using leave-one-out cross-validation, the daily gauge correction was found to improve the median daily correlation by 0.09, constrained by the already strong baseline performance. We anticipate that MSWEP V3 -- accessible at www.gloh2o.org/mswep -- will enable more reliable monitoring, forecasting, and management of water-related risks in a variable and changing climate. |
| title | MSWEP V3: Machine Learning-Powered Global Precipitation Estimates at 0.1$^\circ$ Hourly Resolution (1979-Present) |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2602.01436 |