Precipitation is one of the most important components of the global water and energy cycles, which together regulate the climate system. Future changes in precipitation patterns related to climate change are likely to have the greatest impacts on society. The new generation of geostationary Earth orbit (GEO) satellites provide high-resolution observations and opportunities to improve our understanding of precipitation processes. This study contributes to improved precipitation characterization and retrievals from space by identifying precipitation types (e.g., convective and stratiform) with multispectral observations from the Advanced Baseline Imager (ABI) sensor onboard the GOES-16 satellite. A machine-learning-based classification model is developed by deriving a comprehensive set of features using five ABI channels and numerical weather prediction observations, and trained with the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system as a benchmark. The developed prognostic model shows skillful performance in identifying the occurrence/nonoccurrence of precipitation (accuracy of 97%; Kappa coefficient of 0.9) and precipitation processes, with overall classification accuracy of 76% and Kappa coefficient of 0.56. Challenges exist in separating convective and tropical from other precipitation types. It is suggested to utilize probabilities instead of deterministically separating precipitation types, especially in regions with uncertain classifications. © 2021 Royal Meteorological Society