Evaluation of Multi-Source Precipitation Products in Guangdong Province

Accurate precipitation data are critical for hydrological and climatic studies in Guangdong Province, where complex terrain and frequent extreme rainfall pose substantial challenges. However, the performance of gridded precipitation products is still not well understood. This study evaluates nine products, including gauge-based (CHM_PRE, CN05.1, GMCP, NOAA CPC), satellite-based (IMERG-E, IMERG-F, TMPA RT, TMPA 3B42), and ERA5 reanalysis against NCDC observations from 2001 to 2019 using metrics including trend significance, correlation (R), root mean square error (RMSE), categorical statistics (POD, FAR, ETS), and relative bias across rainfall intensities. The results indicate that, based on validation against NCDC observations, CHM_PRE performs the best across all temporal scales, capturing significant increasing trends (p < 0.05) and achieving the highest consistency with observations at the annual (R = 0.99), monthly (R = 0.99), and daily (R = 0.89) scales. Using CHM_PRE as the reference, CN05.1 shows the highest spatial consistency with it, especially for extreme events. NOAA CPC exhibits the best performance in monthly event detection (ETS = 0.42; BIAS ≈ 1). Satellite products show acceptable performance at the monthly scale but exhibit intensity-dependent biases and high daily variability, with pronounced “light rain overestimation and heavy rain underestimation.” ERA5 shows limitations, particularly in its severe underestimation of extreme precipitation. CHM_PRE is thus identified as the most suitable dataset for Guangdong based on its agreement with NCDC observations. With CHM_PRE as the reference, CN05.1 provides a reliable alternative for spatial analyses; NOAA CPC performs the best in monthly event detection. Satellite products suit monthly use but require daily-scale caution; ERA5 shows a relatively poor performance.