Snow is an integral part of the global water supply and storage system. Snow droughts impact ecological, agricultural, and urban systems by altering the amount and timing of water delivery. These droughts are characterized by a lack of on-the-ground snow (snow water equivalent, SWE) during the cold season and can be caused by low total precipitation (dry) or low proportion of precipitation falling as snow (warm), which is often combined with early melt. The Standardized SWE Index (SWEI) ranks the current status of a given area’s snow water resources compared to normal conditions and identifies the existence, but not the cause, of snow drought. In this work, we use fully coupled simulations from the Energy Exascale Earth System Model (E3SM) to quantify the frequency, severity, and type of snow droughts globally for historical and future CMIP6 scenarios. We compare historical ensemble statistics with reanalysis-based snow drought estimates in the years where data coexist and find that E3SM CMIP6 simulations capture the temporal and spatial distribution of snow droughts from 1980-2015, including the recent increase in snow droughts in several hotspots around the world’s largest water towers (e.g., High Mountain Asia and the Sierra Nevada). In future runs of E3SM, following standard CMIP6 scenarios, the frequency of severe warm snow droughts is expected to increase significantly. These events will have the potential to exacerbate hydrological droughts through multi-year soil moisture deficits. In some hotspots, snow drought conditions are expected to become the new normal within decades, when compared to historical norms. Future SWE and SWEI values are likely to be more centered toward lower values (less snow, more severe drought). E3SM simulations pinpoint snow drought as an emerging global threat to water resources and highlight the need to explore higher resolution future models that better capture complex mountain topography, wildland fires, and snow-forest interactions.