In the summer of 2023, North China experienced a rare extreme precipitation storm due to Typhoons Doksuri and Khanun, leading to significant secondary disasters and highlighting the urgent need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products like Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission have great potential for enhancing forecasts, necessitating a quantitative evaluation before deployment. This study uses a dense rain gauge as a benchmark to assess the accuracy and capability of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products for the 2023 summer extreme precipitation in North China. These satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER) and IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), along with two gauge-corrected products, namely IMERG final run (IMERG_FR) and GSMaP gauge adjusted (GSMaP_Gauge). The results show that (1) GSMaP_MVK, IMERG_LR, and IMERG_FR effectively capture the space distribution of the extreme rainfall, with relatively high correlation coefficients (CCs) of approximately 0.77, 0.75, and 0.79. The IMERG_ER, GSMaP_NRT, and GSMaP_Gauge products exhibit a less accurate spatial pattern capture (CCs about 0.66, 0.73, and 0.67, respectively). Each of the six QPE products tends to underestimate rainfall (RBs < 0%). (2) The IMERG products surpass the corresponding GSMaP products in serial rainfall measurement. IMERG_LR demonstrates superior performance with the lowest root-mean-square error (RMSE) (about 0.38 mm), the highest CC (0.97), and less underestimation (RB about −6.37%). (3) The IMERG products at rainfall rates ≥ 30 mm/h, GSMaP_NRT and GSMaP_MVK products at rainfall rates ≥ 55 mm/h, and GSMaP_Gauge products at ≥ 40 mm/h showed marked limitations in event detection, with a near-zero probability of detection (POD) and a nearly 100% false alarm ratio (FAR). In this extreme precipitation event, caution is needed when using the IMERG and GSMaP products.

In the summer of 2023, North China was hit by an exceptionally intense precipitation storm caused by Typhoons Doksuri and Khanun, resulting in significant secondary disasters and underscoring the critical need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products, such as Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission, show great merits for enhancing forecasts. This study uses a dense rain gauge network as a benchmark to evaluate the performance of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products during the 2023 summer extreme precipitation event in North China. The satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER), IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), as well as two gauge-corrected products IMERG final run (IMERG_FR) and GSMaP gauge-adjusted (GSMaP_Gauge). The results show that the satellite-based QPE products, particularly IMERG_LR and GSMaP_MVK, show good performance in capturing the spatial distribution and overall rainfall amounts during the extreme precipitation event. However, they have significant under-detect high-intensity precipitation events in this region. The IMERG products generally outperform the GSMaP products, especially in terms of temporal rainfall measurement, but all products tend to underestimate rainfall. At high rainfall rates, while the detection ability is high, the false alarm ratios are also significantly elevated for all satellite-based QPE products. These findings highlight the need for further improvement of satellite-based QPE products for more accurate and reliable rainfall estimation.