Abstract: Strengthening the monitoring and analysis of extreme precipitation is crucial for early warning of disaster weather. Based on daily precipitation data from the national basic stations of the China Meteorological Administration from 2000 to 2023, the applicability of three near-real-time daily products of the Global Precipitation Measurement (GPM) for monitoring extreme precipitation in North China during summer is analyzed from the perspectives of extreme precipitation indices and extreme heavy precipitation events. The results indicate the following. First, integrated multisatellite retrievals for GPM (IMERG) products tend to overestimate precipitation, particularly in the Shandong Peninsula, with IMERG_V6L showing a more pronounced overestimation. In contrast, global satellite mapping of precipitation_near real-time, version 6(GSMaP_GNRT6) tends to underestimate precipitation, particularly in the foothill regions. Second, the precipitation amount and intensity indices generally exhibit higher correlations in the transitional regions between mountains and plains compared with other areas, whereas the precipitation duration and heavy precipitation frequency indices show relatively lower correlations in these regions. In addition, in regions with more precipitation, the correlations are higher than in regions with less precipitation. Finally, satellite-derived precipitation can capture extreme precipitation days, with IMERG_V6L performing the best; however, the absolute intensity of precipitation is generally significantly underestimated. Using relative thresholds, such as percentiles and historical ranking order , can reflect the spatial distribution of extreme precipitation, but there is an issue of overestimation in the spatial extent, with GSMaP_GNRT6 showing the most pronounced overestimation. Overall, IMERG and GSMaP have the potential for application in monitoring and analyzing extreme precipitation in North China during summer, but different products should be distinguished and applied according to different regions and application scenarios.