GSMaP_NRT (Near Real Time) is a viable tool to provide satellite-based precipitation data for further analysis. Its usefulness can be evident in the areas where continuous precipitation data is vital. This is why GSMaP_NRT performance has been evaluated globally. In this study, we evaluate its performance in terms of 1) rainfall detection capability based on Probability of Detection (POD) and False Alarm Ratio (FAR) and 2) estimation capability based on correlation coefficient and Root Mean Square Error (RMSE) over the Chaophraya River basin during 2009–2010. A non-realtime GSMaP_MVK (Moving Vector with Kalman filter) is also evaluated. Our results show that, at daily scale, both GSMaP_NRT/GSMaP_MVK performs well in rainy season (POD and FAR can reach 0.75/0.94 and 0.45/0.49, respectively) with acceptable RMSE of 14.64/14.23 mm. GSMaP_NRT tends to under-estimate whereas GSMaP_MVK slightly over-estimates the rain rates with correlation coefficient of 0.70 and 0.75. We conclude that GSMaP_NRT is considered good but not sufficient for near-realtime rainfall monitoring applications; whereas GSMaP_MVK is suitable for climate change studies.