| Summary: | Extreme precipitation events have increasingly happened at global and regional scales as the global climate has changed in recent decades. Accurate quantitative precipitation estimation (QPE) plays an important role in the warning of extreme precipitation events. With hourly rain gauge observations as a reference, this study compares the performance of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) quantitative precipitation estimation (QPE) products over Northern China in 2021. The Probability of Detection (POD), Relative Bias (RB), Root-Mean-Squared Error (RMSE), and Fractional Standard Error (FSE) are among the assessment metrics, as are the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). We examined the spatial distribution of cumulative precipitation and the temporal distribution of hourly average precipitation for three severe precipitation occurrences using these assessment metrics. The IMERG products capture strong precipitation centers that are compatible with the gauge observations, especially in extreme precipitation events in areas with relatively flat terrain and low-altitude (≤1000 m). Both IMERG (National Aeronautics and Space Administration, NASA) and GSMaP (Japan Aerospace Exploration Agency, JAXA) satellite-based QPE products have precipitation peaks in advance (2–4 h) and generally underestimate (overestimate) precipitation when the actual precipitation is heavy (light). The satellite-based QPE products generally overestimate the heavy rainfall caused by non-typhoons and underestimate the heavy rainfall caused by typhoons. The GSMaP products may have the capacity to detect short-term rainstorm events. The accuracy of satellite-based QPE products may be influenced by precipitation intensity, sensors, terrain, and other variables. Therefore, in accordance with our recommendations, more ground rainfall stations should be used to collect actual precipitation data in regions with high levels of spatial heterogeneity and complex topography. The data programmers should strengthen the weights computation retrieval technique and fully utilize infrared (IR)-based data. Furthermore, this study is expected to give helpful feedback to the algorithm developers of IMERG and GSMaP products, as well as those researchers into the use of IMERG and GSMaP satellite-based QPE products in applications.
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