Recently, many people have suffered from the unusual severe weather phenomena such as heavy rains and very wild typhoons. However, it is not easy task to provide reasonable information on these severe weather events. Accordingly it has been eager to find a suitable methodology to provide an early warning against these severe weather events. Quantitative precipitation forecasts and probabilistic quantitative precipitation forecasts (QPFs and PQPFs, respectively) might be one of the most promising methodologies for reasonable warning on the flesh floods. QPFs and PQPFs must be on time because severe weather phenomena have a tendency to be developed very quickly and locally. By these reasons, they may not allow to be announced in a reasonable advanced time. Accordingly it is desired to develop a methodology to provide detail information locally and quickly. A fine-mesh non-hydrostatic mesoscale model can be hired, however, it requires a significant computational resources as well as integration time so that it may not meet the time restriction to be forecasted in operational sense. An alternative way to provide necessary information on fine-mesh rainfall is utilizing a diagnostic rainfall model to avoid heavy computational requirement fine-mesh full-dynamic non-hydrostatic mesoscale model. In this study we examine the capability of diagnostic rainfall model in terms of how well represented the observed several rainfall events and which is the most optimistic resolution of the mesoscale model in which diagnostic rainfall model is nested. Also, we examine the integration time to provide reasonable fine-mesh rainfall information. The diagnostic rainfall model used in this study is the named QPM(Quantitative Precipitation Model), which calculates the rainfall by considering the effect of small-scale topography which is not treated in the mesoscale model. As a result, QPM has a capability to provide fine-mesh rainfall information in terms of time and accuracy compared to full dynamical fine-mesh mesoscale model.