A New Perspective for Charactering the Spatio‐temporal Patterns of the Error in GPM IMERG Over Mainland China

Abstract Evaluation is necessary and informative for processing and applying the precipitation products. However, researchers mainly focus on the statistical indicators of satellite‐based precipitation evaluation over manually determined regions without further considering the geographical information. To reveal the potential connections between geographical features and the error patterns of satellite precipitation estimates, this study focuses on proposing a new perspective for charactering the spatiotemporal patterns of errors in the Integrated Multi‐satellitE Retrievals for global precipitation measurement (GPM; IMERG) based on China Merged Precipitation Analysis over mainland China in the period from 2012 to 2018. We applied a quantitative cluster analysis method to analyze the characteristics of the errors in GPM IMERG based on three geographical features including elevation (Ele), latitude (Lat), and the distance from seashore (DFS). Additionally, the analysis was conducted in both warm and cold seasons to capture the temporal error patterns in IMERG. The results show that: (1) IMERG has the ability to capture spatiotemporal precipitation patterns over mainland China, with relative overestimations mainly over southeastern region; (2) geographical features (Ele, Lat, and DFS) have strong linear relationships with the statistical error indexes (e.g., correlation coefficient) of IMERG; and (3) the spatial patterns of errors in IMERG were automatically determined and demonstrated significant geographical characteristics. Finally, results of this study would provide valuable references for improving the quality of the IMERG over land