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Satellite-based precipitation products (SPPs) have greatly improved their applicability and are expected to offer an alternative to ground-based precipitation estimates in the present and the foreseeable future. There is a strong need for a quantitative evaluation of the usefulness and limitations of SPPs in operational meteorology and hydrology. This study compared two widely used high-resolution SPPs, the Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) in Poyang Lake basin which is located in the middle reach of the Yangtze River in China. The bias of rainfall amount and occurrence frequency under different rainfall intensities and the dependence of SPPs performance on elevation and slope were investigated using different statistical indices. The results revealed that (1) TRMM 3B42 usually underestimates the rainy days and overestimates the average rainfall as well as annual rainfall, while the PERSIANN data were markedly lower than rain gauge data; (2) the rainfall contribution rates were underestimated by TRMM 3B42 in the middle rainfall class but overestimated in the heavy rainfall class, while the opposite trend was observed for PERSIANN; (3) although the temporal distribution characteristics of monthly rainfall were correctly described by both SPPs, PERSIANN tended to suffer a systematic underestimation of rainfall in every month; and (4) the performances of both SPPs had clear dependence on elevation and slope, and their relationships can be fitted using quadratic equations. [ABSTRACT FROM AUTHOR]