LAUSR

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 on board 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 850–500 hPa 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 vapour channel T6.2 with the infrared T11.2 channel for improved precipitation classification. Overall, this study provides guidance to atmospheric and remote‐sensing scientists on a large array of predictors that can be used from geostationary satellites and multispectral sensors for precipitation studies.