Twenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land-cover. When introduced in 1995, the finest resolution global datasets of these variables were on a 0.5° × 0.5° latitude-longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arcsecond × 10-arcsecond latitude-longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude-longitude (approximately 10 km) gridded meteorological climatologies (1981-2019, European Centre for Medium-Range Weather Forecasts [ECMWF] ReAnalysis, 5 th Generation Land [ERA5-Land]) using MicroMet, a spatially distributed, high-resolution, micro-meteorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification 2.0) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).