Spatiotemporal Distribution and Applicability Evaluation of Remote Sensing Precipitation in River Basins Across Mainland China

This research evaluates the performance of the Final Run remote sensing precipitation products from the Integrated Multi-satellite Retrievals for GPM (IMERG-F) in complex terrain river basins (2014–2023). Utilizing decade-long daily precipitation data from 2415 manned national-level ground stations, the evaluation employs eight statistical metrics—probability of detection, false alarm ratio, accuracy, critical success index, Pearson correlation coefficient (PCC), root mean square difference, mean difference, and relative difference—to analyze detection accuracy, correlation, and bias on daily, monthly, and annual scales. The main findings include the following: (1) IMERG-F’s daily precipitation detection capability follows a three-tier spatial pattern (northwest to southeast), aligning with the stepped terrain of China. (2) Stronger correlations (PCC = 0.7–0.9) with gauge data emerge in southeastern regions despite higher biases, while northwestern areas show weaker correlations but fewer deviations. (3) IMERG-F overestimates annual rainy days, but slightly underestimates precipitation intensity compared with ground observations. (4) Annual precipitation estimates exceed gauge measurements, particularly in the Songhua and Liao River Basins (18–20% overestimation). Monthly analysis shows fewer errors during rainy seasons versus winter dry periods, with pronounced seasonal variations in northwestern basins. These findings emphasize the need for terrain-aware calibration to improve satellite precipitation monitoring in hydrologically diverse basins, particularly addressing seasonal and spatial error patterns in water resource management applications in northern China.