High resolution satellite-based rainfall products have been used as an alternative source of data in areas where ground rainfall measurements are not available or missed. The objective of this study is to compare four high-resolution satellite-based rainfall products including: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), African Rainfall Estimation (RFE 2.0), and Tropical Applications of Meteorology using Satellite (TAMSAT) with the ground-based observed rainfall data over the Omo-Gibe River Basin. A total of 17 stations with relatively complete and long time series data were used for comparison. For spatial assessment, standard approach is adopted at different temporal scale over the time window of 2003-2017. Different statistical methods were used to assess their performance in estimating and reproducing rainfall amounts and evaluate rain detection capabilities. Results showed that at mean daily scale good correlation agreement was observed at one station with TAMSAT, RFE, PERSSIAN-CCS, CHRIPS (r = 0.769, r = 0.686, r = 0.627, r = 0.543) respectively; whereas, the remaining stations with the products shows poor correlation with ground observation. However, when the time step increases the accuracy of satellite rainfall products to predict the rainfall event relative to the station value will increase. From different satellite rainfall products which are used in this study the adjusted TAMSAT dataset can be used as an alternative dataset for further analysis in the study area.