Improvements in remote sensing capability and improvements in artificial intelligence have created significant opportunities to advance understanding of precipitation processes. While highly advanced Machine Learning (ML) techniques improve the accuracy of precipitation retrievals, how these observations contribute to our understanding of precipitation processes remains an underexplored research question. In a companion manuscript, a precipitation type prognostic ML model is developed by deriving predictors from the Advanced Baseline Imager (ABI) sensor onboard Geostationary Observing Environmental Satellite (GOES)-16. In this study, these predictors are linked to different precipitation processes. It is observed that satellite observations are important in separating Rain and No-Rain areas. For stratiform precipitation types, predictors related to atmospheric moisture content, such as relative humidity and precipitable water, are the most important predictors, while for convective types, predictors such as 850-500hPa lapse-rate and Convective Available Potential Energy (CAPE) are more important. The diagnostic analysis confirms the benefit of spatial textures derived from ABI observations to improve the classification accuracy. It is recommended to combine the heritage water vapor channel T6.2 with the IR T11.2 channel for improved precipitation classification. There is more than 10% improvement in detection of stratiform and tropical precipitation types compared to using T11.2 alone.