The revolution in weather forecasting (Boers et al., 2014) has led to significant improvement in the simulation of precipitation in synoptic and mesoscales by Numerical Weather Prediction (NWP) models. However, the quantitative precipitation forecast (QPF) on a smaller scale, required for hydrological forecasts remains a challenge even in the latest high resolution operational models (Shrestha et al., 2013;Wang et al., 2016) with unacceptably large mean absolute error (Giannaros et al., 2015). The problem of errors in the QPF appears to be related to (a) displacement of the simulated center of the mesoscale system compared to observed, (b) simulation of the phase of the diurnal cycle of precipitation over land by models a few hours before observed (Dirmeyer et al., 2012) and (c) underestimation of heavy precipitation by almost all climate models (Kendon et al., 2012). As the same factors are also responsible for prediction errors of thunderstorms and extreme rainfall events, it is critical to improve them in models for skillful predictions of hazards associated with the increasing frequency of extreme rainfall events (Goswami et al., 2006). While there is a need for improving all three aspects of precipitation simulation in a model, in this study, we focus only on the "intensity" simulation of a convection-permitting NWP model.It is also known that an adequate "cloud microphysics" parameterization is essential for the simulation of the organization of mesoscale systems (Hazra et al., 2017(Hazra et al., , 2019. The prevailing cloud/rain microphysical processes