We explore the use of three statistical and machine learning methods (a generalized linear model, random forest, and neural network) to predict the occurrence and rain rate distribution of three tropical rain types (deep convective, stratiform, and shallow convective) observed by the radar onboard the GPM satellite over the Pacific. Three-hourly temperature and moisture fields from MERRA-2 were used as predictors. While all three methods perform reasonably well at predicting the occurrence of each rain type, they all struggle to reproduce heavy tailed rain rate distribution for all three types, as well as their spatial patterns. While the neural net is the only method that can produce extreme rain amounts, there is serious overfitting problem even with moderate number of hidden layers. We will discuss challenges and also current direction we are taking to overcome this problem.