Characterizing error structures in precipitation products not only facilitates their proper applications for scientific and practical purposes but also helps improve their retrieval algorithms and processing methods. Despite the fact that multiple precipitation products have been assessed in the literature, factors that affect their error structures remain inadequately addressed. By interpreting 60 binary decision trees, this study disentangles the error characteristics of precipitation products in terms of their spatiotemporal patterns and geographical factors. Three independent precipitation products - two satellite-based and one reanalysis datasets: the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) late run (IMERG-L), Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 uncorrected precipitation output (MERRA2-UC), are evaluated across the contiguous United States from 2010 to 2019. The ground-based Stage IV precipitation dataset is used as the ground truth.

Results indicate that the MERRA2-UC outperforms the IMERG-L and SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the analysis period. Decision trees cross-assess three spatiotemporal factors and find that the underestimation of MERRA2-UC occurs in the east of the Rocky Mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine different geographical characteristics, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover type, topography position index, and aspect are three relatively weak factors.

表征降水产物中的误差结构不仅有助于将其正确应用于科学和实际目的，而且有助于改进其检索算法和处理方法。尽管文献中已经评估了多种降水产物，但影响其误差结构的因素仍未得到充分解决。通过解释 60 个二元决策树，本研究从时空模式和地理因素方面解开了降水产品的误差特征。三个独立的降水产品 - 两个基于卫星的数据集和一个再分析数据集：用于 GPM（全球降水测量）后期运行的综合多卫星检索 (IMERG-L)、土壤水分到降雨-高级 SCATterometer (SM2RAIN-ASCAT)、

结果表明，MERRA2-UC 在分析期间以更高的准确性和更稳定的年际模式优于 IMERG-L 和 SM2RAIN-ASCAT。决策树交叉评估三个时空因素，发现 MERRA2-UC 低估发生在落基山脉东部，SM2RAIN-ASCAT 低估了高纬度地区的降水，尤其是冬季。此外，决策树方法将系统误差归因于九个不同的地理特征，其中到海岸的距离、土壤类型和 DEM 是三个主要特征。另一方面，土地覆被类型、地形位置指数和坡向是三个相对较弱的因素。