In the presentation we provide details on the data processing algorithm, give examples of the daily maps of snow and ice comprising the dataset and compare them with station data and other remote sensing-based snow cover products. Daily maps of snow and ice cover generated with the GMASI algorithm have been applied to estimate the daily, monthly mean and yearly mean continental-scale and hemispherical snow extent. They have been further used to produce the snow and ice cover climatologies including snow and ice monthly mean frequency of occurrence, estimates of the mean snow cover duration over the globe and multiyear trends in the snow and ice extent. We discuss the absolute values and the observed year-to-year variability in the estimated monthly mean and yearly mean snow extent in the last 30 years and compare it with corresponding estimates derived from NOAA Interactive snow charts at 180, 24 and 4 km spatial resolution. It is shown that the existing snow cover climatology based on the coarse, 180 km, resolution interactive snow charts is overestimating the yearly mean continental-scale and Northern Hemisphere snow extent by 10-15%. Time series of the snow extent estimated from the automated and interactive maps demonstrate an obvious positive correlation which noticeably increases with an increasing spatial resolution of interactive products. The two datasets (automated and interactive) agree on the general decreasing trends in the Northern Hemisphere snow extent from late spring to early fall and on a slight increase of the snow extent during the rest of the year. The decreasing trends in the automated dataset were found to be more extensive spanning over a one to two months longer period of the year than in the interactive-based dataset. No meaningful trend in the yearly mean snow extent in the last 30 years has been found in either dataset.