Abstract: CMA Multi-source Precipitation Analysis System (CMPAS), developed by National Meteorological Information Center, provides strong support for meteorological services and forecasts during the flood season. 0.01° sub-products of CMPAS with 1 h and 10 min in time scales during the extreme precipitation in Beijing from 29 July to 2 August of 2023 are assessed, respectively, using rain gauge measurements derived from natural resource stations and hydrologic stations, and data of delayed transmission from weather stations. The purpose is to evaluate the applicability of two products in service and fusion data from multiple departments for further improvement. Results indicate that precipitation measurements from natural resource stations and hydrologic stations are highly consistent with those recorded by weather stations, both in terms of spatial distribution and 1-h precipitation between nearby stations. Moreover, data from natural resource stations and hydrologic stations could supply a more detailed spatial distribution of precipitation in mountainous areas. Assessment based on grade of precipitation indicates that skill scores, including bias, probability of detection, success ratio, and threat score of 1-h product are higher than those of 10-min product, when 24-h precipitation is less than 250.0 mm or 1-h precipitation is less than 10.0 mm. Performances are opposite when 24-h precipitation is no less than 250.0 mm or 1-h precipitation is no less than 20.0 mm. The error statistics shows that root mean square error and mean error of 10-min product are reduced by 50.86 mm (23.4%) and 55.48 mm (27.8%), respectively, and its slope of linear regression and correlation coefficient increase by 0.045 and 0.031, respectively, compared with 1-h product for precipitation of the process which is greater than 600.0 mm. Root mean square error of 10-min product decreases by 10.93 mm (11.9%) for 24-h precipitation amounts no less than 250.0 mm. Root mean square error and mean error are cut down by 0.86 mm and 1.02 mm (5.7% and 10.2%), respectively, for 1-h precipitation no less than 20.0 mm, and the negative mean error is modified significantly at stations with altitudes greater than 800.0 m. It indicates that the underestimation of the analysis product in mountainous areas is improved in 10-min product compared with 1-h product. For optimal product enhancement in Beijing, integrating multi-source data from natural resource and hydrological monitoring stations is essential. More independent tests and control experiments are needed to assess the performance of products under extreme precipitation conditions.