<p>Precipitation plays a vital role in the hydrological cycle and significantly influences weather and climate patterns. However, obtaining accurate and high-resolution precipitation data remains a challenge, particularly in regions with complex terrain. This study assesses the accuracy of four commonly used satellite precipitation products (SPPs)—IMERG, CHIRPS, PERSIANN CDR, and TRMM—by comparing them with rain gauge data across various climate zones in Taiwan from 2000 to 2020. The evaluation is conducted at monthly, seasonal, and regional levels using a comprehensive set of statistical measures, including the Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), relative bias, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, probability of detection, false alarm ratio, critical success index, and the Mann–Kendall test for trend analysis. The results reveal complementary performance characteristics: IMERG excels in precipitation detection with superior correlation coefficients and categorical indices, while CHIRPS demonstrates better magnitude estimation, with higher correlation coefficients and categorical indices, and lower MAE and RMSE. This performance dichotomy highlights the importance of application-specific product selection. Additionally, this study explores the influence of topography and latitude on SPP accuracy, revealing no uniform trends except for TRMM, which demonstrates a significant increasing trend at two locations. The findings provide crucial benchmarks for evaluating satellite precipitation products in complex terrains and offer practical guidance for users and algorithm developers in selecting the most appropriate SPPs for hydrological and meteorological applications. By addressing key limitations and identifying areas for enhancement, this research contributes to refining precipitation estimation techniques and improving their applicability in regions with limited ground-based observations.</p>