Spatial and temporal variations of global floods during the TRMM period (1998–2013) are explored by means of the outputs of the Dominant River Routing Integrated with VIC Environment model (DRIVE) driven by the precipitation rates from the TRMM Multisatellite Precipitation Analysis (TMPA). Climatological and seasonal mean features of floods including frequency (FF), duration (FD), and mean and total intensity (FI and FTI) are examined and further compared to those for a variety of precipitation indices derived from the daily TMPA rain rates. In general, floods and precipitation manifest similar spatial distributions, confirming that more precipitation (both amount and frequency) often indicates higher probability of floods. However, different flood indices can be associated with different precipitation characteristics with a highly region-dependent distribution. FF and FD tend to be more related to daily precipitation frequency globally, especially the mid- to high-end precipitation frequencies (F10, F25, F50). However, FI and FTI tend to be more associated with the mean volume/magnitude of those (extreme) daily precipitation events (Pr10 and Pr25). Nonetheless, daily precipitation intensity except the very high end one (R50) generally has a relatively weak effect on floods. The precipitation–flood relations at the 10 large regions are further examined, providing an improved understanding of precipitation-related flood-generating mechanisms in different locations. On the interannual time scale, El Niño–Southern Oscillation (ENSO) can significantly affect floods in many flood-prone zones. However, it is noted that even though the ENSO effect on floods is mostly through modulating various aspects of precipitation events, significant ENSO signals in precipitation cannot always translate to an effective, simultaneous impact on floods.

This study aims to systematically evaluate the accuracy and applicability of GPM satellite precipitation products (IMERG-E, IMERG-L, and IMERG-F) with varying time lags at different spatial and temporal scales over mainland China. We use quantitative statistical indicators, including correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), mean daily precipitation, probability of detection (POD), false alarm rate (FAR), bias, and equitable threat score (ETS), based on observations from 2419 national gauge sites. The results show that GPM satellite precipitation products perform well in eastern and southern humid regions of China, with relatively poorer performance in western and northern regions in terms of spatial distribution. It reflects the sensitivity of GPM precipitation retrieval algorithm to climate and precipitation type, topography, density, and quality of ground observation across different latitudes. Despite the design of GPM for different forms of precipitation, IMERG products perform the best in summer and the worst in winter, indicating that estimating snowfalls via satellite is still challenging. In terms of precipitation intensity, IMERG products significantly improve performance for light and no rain (POD ≥ 0.7), but errors gradually increase for moderate, heavy, and torrential rain, due to the saturation tendency of satellite echoes. Overall, we comprehensively evaluate the IMERG products, revealing the distinct characteristics at various spatial–temporal scales focusing on rainfall accumulations over mainland China. This study provides an important reference for other similar satellite-based precipitation products. It also helps the parameter optimization of hydrological modelling, especially under extreme precipitation conditions, to enhance the accuracy of flood simulation.