Extreme precipitation events, including those associated with weather fronts, have wide-ranging impacts. Machine learning-based detection algorithms can help with the automated classification of the synoptic-scale weather features that produce extreme precipitation events, such as fronts. Here we use a deep learning algorithm to identify weather fronts in high resolution Community Earth System Model (CESM) simulations over North America and evaluate the results using observational and reanalysis products. We further compare results between CESM simulations using present-day and future climate forcing, to study how these features might change with climate change. We find that detected front frequencies in CESM have seasonally varying spatial patterns and responses to climate change and are found to be associated with modeled changes in large scale circulation such as the jet stream. We also associate the detected fronts with precipitation and find that total and extreme frontal precipitation mostly decreases with climate change, with some seasonal and regional differences. These changes are largely driven by changes in the frequency of different front types, demonstrating that extreme precipitation has seasonally varying sources and mechanisms that will continue to evolve with climate change.