Reliable rainfall extreme simulation is critical for understanding extreme precipitation patterns and guiding climate adaptation strategies in regions like Jemma sub-basin, Ethiopia. This study evaluates the performance of five satellite/reanalysis rainfall datasets such as the Climate Hazards Group Infrared Precipitation with Stations, version 2.0 (CHIRPS v2.0), Tropical Applications of Meteorology using Satellite and ground-based observations, version 3.1 (TAMSAT v3.1), Multi-Source Weighted-Ensemble Precipitation, version 2.8 (MSWEP v2.8), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA v2) and ERA5 against observed data (2000-2023). The evaluation focused on simulating daily rainfall, detecting rainfall events, and capturing extreme rainfall indices across sub-tropical and temperate agroecological zones (AEZs) of Jemma sub-basin. For this study point to pixel evaluation approach was used and the Statistical metrics, including continuous statistical metrics such as root mean square error (RMSE), percent bias (PBIAS), Kling–Gupta efficiency (KGE’), and correlation coefficient (R), and categorical statistical metrics such as probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias Index (FBI), were used to assess their performance by give the rank after calculating comprehensive rating index (CRI) value. The results revealed that based on CRI value MSWEP v2.8 has first ranked better performance than others products over sub-tropical and temperate AEZs at the daily time scales and also it is a better detecting capability of rainfall events. CHIRPS v2.0 demonstrated superior performance across different indices by holding first rank based on CRI value, proving particularly effective in simulating extreme rainfall indices. MSWEP v2.8 ranked second, showing notable strengths in the temperate AEZ. In contrast, ERA5 and MERRA v2 exhibited substantial biases and weaker correlations, indicating limited applicability. These findings highlight the importance of selecting region-specific rainfall datasets to improve disaster management, water resource planning, and sustainable agriculture in climatevulnerable regions like the Jemma sub-basin.