Abstract: Satellite precipitation data with high temporal and spatial resolution can effectively address the shortcomings of ground-based observations, playing a pivotal role in precipitation monitoring and hydrological modeling within the Yangtze River Basin. This study aims to evaluate the performance of two satellite precipitation products (GSMaP and IMERG) at hourly, daily and monthly time scales by a series of metrics, such as root mean square error (RMSE), relative root mean square error (RRMSE), correlation coefficient (CC), relative error (Bias), hit rate, false alarm rate, and critical success index, in comparison with ground-based observations along the Yangtze River Basin during 2014-2021. The results are as follows. (1) The overall error metrics of the two GPM satellite precipitation products are basically the same, with an average RMSE of about 40 mm, 7 mm, and 0.9 mm at the monthly, daily, and hourly scales, and an average RRMSE of about 0.4, 2, and 7, respectively. The average correlation coefficients are about 0.9, 0.7, and 0.4, respectively. Both GSMaP and IMERG show an overall overestimation of monthly and daily precipitation. (2) Both GSMaP and IMERG exhibit better performance on a monthly scale compared to daily and hourly scale. Furthermore, they perform better in the middle and lower of the Yangtze River than in the upper regions. (3) On monthly, daily, and hourly scales, GSMaP precipitation produrcts outperform IMERG in terms of errors, bias, and hit rate, while the IMERG demenstrates better correlation and false alarm rate than GSMaP on hourly scale.Overall, the GSMaP precipitation demonstrates higher accuracy than IMERG and can capture the daily and monthly precipitation characteristics in the Yangtze River basin more precisely.