Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction.