Snowpack is a critical component of the mountainous hydrologic cycle.  Water management, particularly in the western US, has built infrastructure and implemented decision points in the water-energy system to capitalize on seasonal snowmelt.   Abrupt snowmelt, whether through rain-on-snow or snow-heatwave interactions, are two particularly difficult snow extremes to manage for and have historically been thought of as low-likelihood high impact (LLHI) events.  Climate change will undoubtedly alter abrupt snowmelt characteristics (e.g., intensity, duration, and spatial extent) and their likelihood of occurrence.  In the CASCADE and HyperFACETS projects, we aim to identify and track the drivers of LLHI snow extremes and distill how sensitive snowmelt responses are to climate internal variability and future warming levels of interest.  We will present ongoing work where we leverage DOE-funded models (e.g., E3SM) and data products (e.g., NOAA-CIRES-DOE 20th Century Reanalysis) to anticipate how LLHI snow loss events respond to different sources of uncertainty (e.g., internal variability, structural, and scenario) in space and time.  The ultimate goal of this work is to help water managers and flood planners anticipate the changing characteristics of future snowmelt extremes.