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A quantitative and physical understanding of satellite rainfall uncertainties provides meaningful guidance on improving algorithms to advance hydrologic prediction. The aim of this study is to characterize satellite rainfall errors and their impact on hydrologic fluxes based on fundamental governing factors that dictate the accuracy of passive remote sensing of precipitation. These governing factors are land features-comprising topography (elevation)-and climate type, representing the average ambient atmospheric conditions. First, the study examines satellite rainfall errors of three major products, 3B42RT, Climate prediction center MORHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), by breaking the errors down into independent components (hit, miss-rain, and false-rain biases) and investigating their contribution to runoff and soil moisture errors. The uncertainties of three satellite rainfall products are explored for five regions of the Mississippi River basin that are categorized grid cell by grid cell (at the native spatial resolution of satellite products) based on topography and climate. It is found that total and hit biases dictate the temporal trend of soil moisture and runoff errors, respectively. Miss-rain and hit biases are the leading errors in the 3B42RT and CMORPH products, respectively, whereas false-rain bias is a pervasive problem of the PERSIANN product. For 3B42RT and CMORPH, about 50%-60% of grid cells are influenced by the total bias during winter and 60%-70% of grid cells during summer. For PERSIANN, about 70%-80% of the grid cells are marked by total bias during the summer and winter seasons. False-rain bias gradually increases from lowland to highland regions universally for all three satellite rainfall products. Overall, the study reveals that satellite rainfall uncertainty is dependent more on topography than the climate of the region. This study's results- indicate that it is now worthwhile to assimilate the static knowledge of topography in the satellite estimation of precipitation to minimize the uncertainty in anticipation of the Global Precipitation Measurement mission.