: Hydrological models have become practical tools to understand impacts in water resources and to support the development of management policies. Precipitation is a major driving force of hydrological processes and is one of the main input datasets for hydrological models. However, gauge measurements have several issues, such as incomplete areal coverage and deficiencies over most tropical regions. The precipitation data obtained by remote sensing is an alternative in areas where data is scarce or not available, like in the tropics. However, the feasibility of using satellite-based precipitation products for simulating streamflow needs to be verified, for different hydrological models and basins. Congo River Basin is one of the least studied major river basins in the world and suffers from the scarcity and difficulty in accessing rain gauge data, which makes satellite precipitation estimates necessary for hydrological studies. In this study, we analyzed twenty-three satellite-based precipitation products, acquired from Frequent Rainfall Observations on GridS (FROGS) daily precipitation database. We evaluated these precipitation products over the Congo River Basin using the hydrological model SWAT (Solid & Water Assessment Tool) for streamflow and water balance components at basin scale. Our findings showed that the products based on satellite-only source tend to overestimate the rainy season peaks in comparison with the 3B42_V7 product. On the other hand, the satellite products that consider gauge calibration presented better agreements between each other. The hydrological model was able to reproduce the general precipitation products characteristics, while the gauge-adjusted satellite products performed better than those without gauge adjustments. Thus, the overall precipitation patterns have a crucial effect on model’s performance and leads to different streamflow and water balance components values. The choice of rainfall product has a significant importance in the interpretation of the simulated hydrological cycle.