The objective of this research is to evaluate daily rain rates derived from three widely used high-resolution satellite precipitation products (PERSIANN, TMPA-3B42V7, and TMPA-3B42RT) using rain gauge observations over the entire country of Iran. Evaluations are implemented for 47 comprehensive daily rainfall events during the winter and spring seasons from 2003 to 2006. These events are selected because each encompasses more than 50% of the country’s area. In this study, daily rainfall observations derived from 1180 rain gauges distributed throughout the country are employed as reference surface data. Six statistical indices: bias, multiplicative bias (MBias), relative bias (RBias), mean absolute error (MAE), root mean square error (RMSE), and linear correlation coefficient (CC), as well as a contingency table are applied to evaluate the satellite rainfall estimates qualitatively. The spatially averaged results over the entire country indicate that 3B42V7, with an average bias value of –1.47 mmd⁻¹, RBias of –13.6%, MAE of 4.5 mmd⁻¹, RMSE of 6.5 mmd⁻¹, and CC of 0.61, leads to better estimates of daily precipitation than those of PERSIANN and 3B42RT. Furthermore, PERSIANN with an average MBias value of 0.56 tends to underestimate precipitation, while 3B42V7 and 3B42RT with average MBias values of 0.86 and 1.02, respectively, demonstrate a reasonable agreement in regard to rainfall estimations with the rain gauge data. With respect to the categorical verification technique implemented in this study, PERSIANN exhibits better results associated with the probability of detection of rainfall events; however, its false alarm ratio is worse than that of 3B42V7 and 3B42RT.

High-resolution, satellite-retrieved precipitation products are useful input data for hydrological predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. In this study, four widely-used satellite rainfall estimates (TMPA-3B42V7, TMPA-3B42RT, PERSIANN, and CMORPH) are evaluated on a dense raingauge network over six regions in Iran with various physiographic and climate conditions. Assessments are implemented at the daily scale for different seasons during the 5-year period 2003–2008. Overall, the results show that 3B42V7 leads to better performance than the other three products over different terrains. According to the relative bias (RBias), one of the verification metrics used in this study; 3B42V7, with an average value of 13.43% over all the regions, matches best with the raingauge observations, while PERSIANN and 3B42RT overestimate precipitation by 78.13% and 31%, respectively. But, CMORPH with RBias of −17.6% tends to underestimate the rainfall amount. Furthermore, the evaluations over different seasons indicate that the best performance for PERSIANN and both TMPA products is during the winter, while for CMORPH it is during the autumn season. From the critical success index (CSI), used to assess the rain detecting skill of satellite products, one can conclude that PERSIANN leads to better estimations during the winter and summer, 3B42RT during the spring, and CMORPH during the autumn. Generally, the analyses implemented in this research provide quantitative information of error characteristics associated with satellite precipitation products over different parts of Iran and thus will offer users a better understanding of satellite rainfall estimates’ applicability in this area.