Extreme precipitation events, including those associated with weather fronts, have wide-ranging impacts across the world. 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 validate the results using observational and reanalysis products. We further compare results between CESM simulations using present-day and future climate forcing, to study how fronts and extreme precipitation 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 extreme precipitation, and find that extreme precipitation associated with fronts mostly decreases with climate change, with some seasonal and regional differences. These changes appear to be largely driven by changes in the frequency of fronts, especially in Northern Hemisphere winter, demonstrating that extreme precipitation has seasonally varying sources and mechanisms that will continue to evolve with climate change.