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 degree 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.

翻译：卫星反演和再分析生成的多源降水产品被广泛应用于水文气候监测，但其空间异质性偏差以及对极端降水事件的有限捕捉能力仍制约着其水文应用价值。本文开发了一种基于双阶段TransUNet的多源降水融合框架，该框架整合了六种多源降水产品与四种ERA5近地表物理预测因子。第一阶段分类器估算逐日降水发生概率，第二阶段回归器融合分类器输出与所有预测因子，以0.25度分辨率生成2001-2020年中国区域的逐日降水量估算。与多种深度学习和混合基准模型的对比评估表明，TransUNet-TransUNet架构取得了最佳的季节性性能（相关系数R = 0.75；均方根误差RMSE = 2.70毫米/天），并相较于单一回归器设置提升了稳健性。对于强降水事件（>25毫米/天），该框架提高了中国东部大部分区域的公平威胁评分，并更好地再现了2021年7月郑州特大暴雨的空间分布特征，表明其在季节性平均校正之外，增强了对极端事件的探测能力。基于TPHiPr数据在青藏高原的独立评估进一步支持了该框架在数据稀缺区域的适用性。SHAP分析揭示了降水发生概率和地表气压的重要性，提供了具备物理解释性的诊断依据。所提出的框架为降水融合和极端事件评估提供了一种可扩展且可解释的方法。