Abstract: The China Meteorological Administration Mesoscale Model (CMA-MESO) serves as a critical operational system for precipitation forecasting. However, its eight-times-daily rolling updates present new challenges for forecast evaluation. A comprehensive understanding of mean bias characteristics, variations across different forecast lead times, and error sources is key to model improvement. This study systematically evaluated the precipitation forecast performance of the CMA-MESO over East China during the summer of 2023. The key findings are as follows: (1) While the averaged multiple forecasts generally reproduced the spatial distribution patterns of observed precipitation, systematic biases existed in precipitation intensity and frequency. (2) The model significantly overestimated the precipitation amount for moderate-and-above rainfall events, with 3.7% underestimation of the frequency of no-rainfall events but 2.6% overestimation of the frequency of light rain events, along with elevated false alarm rates for heavy-and-above rainfall events. (3) The forecast performance showed a distinct lead-time dependence, with precipitation biases largest in 3～6 h nowcasting, gradually decreasing to a minimum in 21～24 h forecasts, and then increasing again in the 24～36 h range. (4) Physical analysis revealed that the nowcasting overestimation stemmed from excessive hydrometeor introduction during cloud analysis, and the overestimation in the forecast after 24 h resulted from excessive low-level wind speeds that promoted moisture transport to the target region.