AMSRE is a satellite-based dataset that includes data collected by microwave scanning radiometers on the Aqua satellite of the National Aeronautics and Space Administration (NASA) Earth Observation System and the GCOM-W1 satellite of the Japan Aerospace Exploration Agency (JAXA) (https://nsidc.org/data/ae_dysno). The AMSRE from NASA (AMSR-E) provides global daily SWE data from 19 June, 2002, to 3 October, 2011, and 130 the AMSRE from JAXA (AMSR2) has been providing global daily SWE data from 2 July, 2012, to the present.

 The GlonSnow SWE products is a synergistic datasets that combines satellite passive microwave data (from sensors like SSM/I and AMSR-E) with ground-based weather station snow depth observations using an advanced data assimilation scheme (https://www.globsnow.info/).

 GLDAS is a modeling system developed by NASA Goddard Space Flight Center (GSFC) and other partners. It ingests satellite-based and ground-based observations to force multiple land surface models (e.g., Noah, VIC) to generate optimal fields of land surface states, including SWE. The system configuration and forcing data are described in technical documentation.

 The GLDAS SWE was downloaded from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). ERA5-Land is a global land surface reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).

 It is generated by replaying the land component of the ECMWF's ERA5 climate reanalysis (https://cds.climate.copernicus.eu/datasets/derived-era5-land-daily-statistics?tab=download). Moreover, the SWEML dataset (version3.0) was is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 0.25˚ (approximately 25km). It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding Antarctica. The dataset is provided in NetCDF format, organized by year. Each year contains daily SWE data, including leap days in leap years (https://zenodo.org/records/16822772)

 We calculated the error metrics to evaluate the accuracy by comparing SWE dataset and datesets with observations. The root meansquare error (RMSE), mean absolute error (MAE), Pearson correlation coefficient (R) and bias were adopted to assess theaccuracy of the SWE dataset. To ensure spatiotemporal consistency during the data fusion process, the validation scope was confined to regions north of 66°34'N. Moreover, to validate the accuracy in spatial of snow water equivalent data, cross-validation was performed using three reference datasets: GLDAS, ESAGB, and AMSRE.