A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates

A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2026 V1 Latest version Share on A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates Authors : Yuchen Ye 0009-0004-7433-3163 [email protected] , Zixuan Qi , Shixuan Li , Wei Qi , Yanpeng Cai , and Chaoxia Yuan Authors Info & Affiliations https://doi.org/10.22541/au.177023531.11868286/v1 Published Journal of Hydrology: Regional Studies Version of record Peer review timeline 110 views 64 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Multi-source precipitation products (MSPs) from satellite retrievals and reanalysis are widely used for hydroclimatic monitoring, yet spatially heterogeneous biases and limited skill for extremes still constrain their hydrologic utility. Here we develop a dual-stage TransUNet-based multi-source precipitation merging framework (DDL-MSPMF) that integrates six MSPs with four ERA5 near-surface physical predictors. A first-stage classifier estimates daily precipitation occurrence probability, and a second-stage regressor fuses the classifier outputs together with all predictors to estimate daily precipitation amount at 0.25° resolution over China for 2001–2020. Benchmarking against multiple deep learning and hybrid baselines shows that the TransUNet – TransUNet configuration yields the best seasonal performance (R = 0.75; RMSE = 2.70 mm/day) and improves robustness relative to a single-regressor setting. For heavy precipitation (>25 mm/day), DDL-MSPMF increases equitable threat scores across most regions of eastern China and better reproduces the spatial pattern of the July 2021 Zhengzhou rainstorm, indicating enhanced extreme-event detection beyond seasonal-mean corrections. Independent evaluation over the Qinghai–Tibet Plateau using TPHiPr further supports its applicability in data-scarce regions. SHAP analysis highlights the importance of precipitation occurrence probabilities and surface pressure, providing physically interpretable diagnostics. The proposed framework offers a scalable and explainable approach for precipitation fusion and extreme-event assessment. Supplementary Material File (ddl-mspmf.pdf) Download 4.37 MB Information & Authors Information Version history V1 Version 1 04 February 2026 Peer review timeline Published Journal of Hydrology: Regional Studies Version of Record 1 Jun 2026 Published Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords atmospheric sciences ddl-mspmf deep learning extreme precipitation events hybrid model multi-source precipitation fusion Authors Affiliations Yuchen Ye 0009-0004-7433-3163 [email protected] View all articles by this author Zixuan Qi View all articles by this author Shixuan Li View all articles by this author Wei Qi View all articles by this author Yanpeng Cai View all articles by this author Chaoxia Yuan View all articles by this author Funding Information National Natural Science Foundation of China 52439005 Yanpeng Cai Metrics & Citations Metrics Article Usage 110 views 64 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yuchen Ye, Zixuan Qi, Shixuan Li, et al. A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates. Authorea . 04 February 2026. DOI: https://doi.org/10.22541/au.177023531.11868286/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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