Soil erosion is one of the most serious problems that cause environmental degradation and loss of agricultural productivity in semiarid regions. To investigate the applicability of satellite-estimated data for assessing rainfall and erosivity in the eastern portion of the Brazilian semiarid region, data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR) were evaluated and compared with measured rainfall data. Rainfall dynamics simultaneously considering precipitation temporal series and geomorphology were analyzed. To evaluate satellite-estimated rainfall data and estimated erosivity, the correlation coefficient (R), relative bias (BIAS), average error (ME), and root mean square error (RMSE) were used based on rain gauge-measured data. The results showed that both estimated rainfall datasets perform well when compared to the rain gauge-measured rainfall data. CHIRPS-estimated rainfall data were slightly more accurate on a monthly scale and captured the spatial variability of the rainfall when compared to the other dataset. Finally, the study pointed to CHIRPS- and PERSIANN-CDR-estimated rainfall data as good alternatives to estimate rainfall and erosivity in a Brazilian semiarid region.