In this study, we present a new bias correction algorithm, which combines conventionally used real time calibration method and statistical transformation method. Real time calibration method estimates calibration coefficient of GSMaP for cells having ground observations and distributes the coefficient with considering distance and elevation as varying weight. Statistical transformation method transforms temporal frequency of GSMaP, so that the histogram has the same statistical distribution of ground observations. Performance of new method is compared against conventional methods through hydrological modeling of recent flood events observed in Sri Lanka. Results show that the real time calibration method has large uncertainty as it considers only the daily snap shot and disadvantage can be seen from ignoring the historical GSMaP errors. It is also found that the performance of statistical transformation method is affected by the length of observation records being used to create the temporal histogram. By contrast the suggested method, which considers both the spatial and temporal GSMaP, shows more robust and accurate results. We may further test the applicability of the new method in different regions and with different observation data for practical application.