Comparison Analysis of Thirteen Global Precipitation Datasets over Mainland China

Various global precipitation datasets have been used in precipitation-related fields such as hydrology, meteorology, climatology, and ecology to achieve different research objectives. Error analysis is an integral part before applying them to operational fields. However, the growing number of precipitation products and the absence of comprehensive error comparison research jointly impede users in distinguishing product-specific error patterns and constrain developers from enhancing precipitation estimation accuracy. To address this issue, we performed error analysis and comparison of thirteen global precipitation products—categorized as delayed time (DT), near real-time (NRT), and real-time (RT) types—across mainland China. Results revealed that GSMaP-Gauge (Gauge-adjusted Global Satellite Mapping of Precipitation) performed best in terms of detection indicators, while MGP (Multi-source merged global precipitation product) performed best in estimating precipitation accuracy. However, IMERG-Final (Integrated Multisatellite Retrievals for Global Precipitation Measurement Final Run) proved ineffective in reducing the overestimations of both storm and light precipitation events in regions of complex topography. Furthermore, two DT products (i.e., ERA5 (Fifth generation of ECMWF atmospheric reanalyses of the global climate) and MGP) overestimated the frequency of light precipitation events, with relative rainfall occurrence biases exceeding 80%. This bias is attributable to both false detections and the misclassification of high intensity rainfall as light precipitation. Although GSMaP-NOW (based exclusively on passive microwave data) detected precipitation more effectively than the infrared-only PDIRNow (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)—Dynamic Infrared Rain Rate (Now)), it achieved lower accuracy. This discrepancy reflects the tradeoff between the higher precipitation sensitivity of passive microwave observations and their sparse temporal sampling, compared with the continuous coverage provided by infrared data. Finally, our findings indicated that current evaluation approaches do not reliably determine the optimal precipitation product, since product superiority is contingent upon the selected error metric. This underscores the urgent need to develop theoretically grounded and operationally reliable methods for selecting optimal precipitation products to support data users in deriving robust and reliable conclusions in hydrology, meteorology, and ecology.