Satellite precipitation (rainfall) products have become increasingly valuable in the delivery of climate services in recent years, particularly in Africa, where the network of synoptic stations has been gradually declining. The Satellites-based Rainfall Estimates (SREs) are estimates and measurements; thus, accuracy is of major concern. This study gives a detailed long-term comparison and evaluation of nine (9) SREs from 2001 to 2020 over East Africa. The study used 105 rain gauge observations and gridded 9 SRE products. The statistical methods such as rainfall totals, annual cycle, 24 continuous, categorical, and volumetric metrics, scatter plots, the Cumulative Distribution Function (CDF), and colored code portraits were employed to assess the temporal and spatial patterns of SREs performance. The evaluation was conducted at each 105-rain gauge spatially and in five sub-regions for different metrics and performance comparisons during March–May (MAM), June–September (JJAS), and October- December (OND). Our findings demonstrate that relying on a single metric for validating the performance of SREs is not sufficient. Instead, it is necessary to utilize multiple metrics to assess rainfall performance, especially in areas with complex topography such as mountains and diverse climatic zones. The spatial patterns of validation of nine SREs showed CMORPH RT, CHIRPS v2.0, CPC-RFEv2, and GPM-IMERG are the top four best-performing SREs. The CMORPH RT emerged the best-performing SRE, followed by CHIRPS v2.0. Also, the satellite products tend to slightly underestimate the rainfall throughout the region. Geographically, SREs performed well over highlands compared to desert and semi-arid regions, while seasonally, the accuracy of SRE was lower over JJAS compared to MAM and OND. This study further demonstrated that the density and distribution of synoptic stations (rain gauges) in a country play a significant role in the accuracy of validation. These findings show that SREs play complementary roles in the accurate monitoring and analysis of precipitation and provide comprehensive coverage, especially in remote areas where ground-based measurements are sparse. These findings serve as guidance to climate service providers and end-users on how to select suitable alternative rainfall datasets for different applications and feedback to the algorithm developers to improve the SRE products.