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Snow cover is a key hydrological variable, critical to understanding water cycles and informing management decisions around resource extraction and recreational activities. Remote sensing open-access data and cloud-based computing platforms are two innovative tools for snow cover estimation. In this paper, we present SnowWarp, a processing framework that uses Google Earth Engine and the R programming languages to combine Landsat 30 m with MODIS 500 m satellite imagery and produce daily-30-m spatial resolution snow cover data anywhere globally. SnowWarp was applied in an alpine catchment in Northern Italy from 2000-2019 and validated using hydrometeorological datasets. Strong correlations between snow cover and ground data were found with correlations in terms of R up to −0.84 for temperature, −0.17 for precipitation, 0.74 for snow depth, and −0.43 for streamflow. The SnowWarp tool is an open-source framework enabling users to map fine spatial and temporal dynamics of snow cover to the ecosystem and hydrological monitoring. • The new open-source SnowWarp tool, utilizing Google Earth Engine and R is presented. • Daily snow cover data at 30 m spatial resolution can be calculated globally. • SnowWarp exploits MODIS and Landsat remote sensing data and implements a dynamic time warping algorithm. • SnowWarp products were generated and tested in an Alpine area in Northern Italy. • Snow cover dynamics were related to different hydro-meteorological variables. [ABSTRACT FROM AUTHOR]