融合六种降水数据，提升中国区域极端降水预测能力
A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates
- 分两阶段：先判雨再估量，结合气象物理因子融合多源数据
- 季节均值相关系数达0.75，极端降水（>25mm/天）误报率显著降低
- 模型可解释性强，适合干旱半干旱区及极端天气研究者使用
基于卫星反演和再分析的多源降水产品（MSPs）广泛用于水文气候监测，但空间异质性偏差和极端事件预测能力不足限制了其应用。本文提出双阶段TransUNet框架（DDL-MSPMF），融合六种MSPs与四种ERA5近地表物理变量。第一阶段分类器估计逐日降水发生概率，第二阶段回归器结合分类结果与全部变量，在0.25度分辨率下对2001–2020年中国区域进行逐日降水估算。相比多种深度学习与混合基线，该方法在季节尺度上表现最优（R=0.75；RMSE=2.70 mm/day），且优于单一回归设置。对于强降水（>25 mm/day），DDL-MSPMF在华东多数地区提升了公平威胁评分，并更准确再现2021年郑州特大暴雨的空间分布特征，表明其在极端事件检测上超越季节平均修正。在青藏高原使用TPHiPr独立验证也证实其在数据稀疏区的适用性。SHAP分析揭示降水发生概率与地表气压的重要性，提供可解释的物理诊断。该框架为降水融合与极端事件评估提供了可扩展、可解释的新范式。
原文摘要 · Abstract (English)
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.
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