Extreme precipitation events continue to have wide-ranging impacts across the world. Rainfall associated with atmospheric rivers, tropical cyclones, mesoscale convective systems, and fronts can cause devastation to communities and ecosystems. Machine learning-based detection algorithms can help with the automated classification of the synoptic weather features that produce extreme precipitation events. Here we use new and existing machine learning algorithms to identify these types of systems in high resolution Community Earth System Model (CESM) output, and validate the results using observational and reanalysis products. We then associate the detected features with precipitation extremes to better understand the sources and mechanisms of extreme precipitation events. We further compare results between CESM simulations using present-day and future climate forcing, to study how extreme events might change and evolve with climate change.