Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 in lieu of the pre-existing simplified Arakawa–Schubert (CTRL) cumulus scheme and has shown essential improvements in different large scale features of tropical convection. One of the features of earlier SMCM is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. In this present study, a new cloud type, namely shallow cloud, is included along with the existing three cloud types to make the model more realistic. The cloud population statistics of four cloud types (shallow, congestus, deep, and stratiform) are taken from Indian (Mandhardev) radar observations and a Bayesian inference technique is used to generate key time scale parameters required for the SMCM as implemented in CFSv2 (hereafter CFSSMCM-4cloud). The 4-cloud simulation improves many aspects of the mean state climate compared to CTRL, and 3-cloud (CFSSMCM-3cloud, where the three most common cloud types are considered) simulation. Significant improvement is noted in the rainfall PDF over the global tropics. The global distribution of different clouds, mainly low-level and mid-level clouds, is also improved. The 4-cloud simulation shows significant improvement with respect to the double ITCZ (The Intertropical Convergence Zone) problem as well as overall organized convection. The convective and large-scale rainfall simulation is investigated in detail.