The northeastern coast of the United States is particularly vulnerable to impacts from extratropical cyclones during winter months, which produce heavy precipitation, high winds, and coastal flooding. These impacts are amplified by the proximity of major population centers to common storm tracks and include risks to health and welfare, massive transportation disruption, lost spending productivity, power outages, and structural damage. Historically, understanding regional snowfall in climate models has generally centered around seasonal mean climatologies even though major impacts typically occur at the scales of hours to days.

To quantify discrete snowstorms at the event level, we describe a new objective detection algorithm for gridded data based on the Regional Snowfall Index (RSI) produced by NOAA's National Centers for Environmental Information. The algorithm uses 6-hourly precipitation to collocate storm-integrated snowfall with population density to produce a distribution of snowstorms with societally relevant impacts. The algorithm is tested on the Community Earth System Model (CESM) Large Ensemble Project (LENS) data. Present day distributions of snowfall events is well-replicated within the ensemble. We discuss classification sensitivities to assumptions made in determining precipitation phase and snow water equivalent. We also explore projected reductions in mid-century and end-of-century snowstorms due to changes in snowfall rates and precipitation phase, as well as highlight potential improvements in storm representation from refined horizontal resolution in model simulations.