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Although passive microwave remote sensing has been widely used for snow depth (SD) retrieval, its coarse spatial resolution has led to large uncertainties in various applications, particularly in mountainous regions. Therefore, downscaling passive microwave data is an effective way to improve the accuracy of regional SD monitoring. In this study, we developed a SD downscaling algorithm based on multisource remote sensing data, generating a long-time series of daily 500 m resolution SD data from 1980 to 2020 over the Tibetan Plateau (TP). The downscaling SD product improved the spatial resolution and significantly reduced the overestimation of the SD for the TP. Based on this product, the spatial pattern and the spatiotemporal trend of the SD were analyzed by the Theil-Sen median method and the Mann–Kendall test. SD on the TP showed clear spatial heterogeneity by vertical zonal distribution, bimodal distribution features changing with longitude, and a thick SD distribution pattern at 30-32°N. The annual average SD, maximum SD, and accumulation of SD on the TP from 1980 to 2020 were 1.02 cm, 5.91 cm, and 39.45 cm, respectively, with trends of −0.06 cm/10a, −0.44 cm/10a, and 1.80 cm/10a (P > 0.05), respectively. SD on the TP increases with elevation above 5.5 km in mountainous areas and decreases in areas below 5.5 km. [ABSTRACT FROM AUTHOR]