**6. Conclusions**

In this study, we developed an improved statistical downscaling method to obtain SD data with higher spatial resolution for a comprehensive study on regional LSSP of the SGP, a specifically selected water source area of China's Yellow River. It was shown that the improved downscaling method can be used to effectively optimize both the spatial resolution and data accuracy of SD. Statistics from 2003 to 2018 revealed an overall reduction in the SGP's LSSP, especially the two main indices of SD and SCD, which varied at negative rates of −0.06 cm/a and −0.37 d/a, respectively. In terms of terrain, the LSSP of the SGP generally presented a pattern of "decrease in higher and steeper areas corresponding to a shortened snow duration, increase in lower and flatter areas corresponding to a relatively lengthened duration". Climatically, given a 1% increase in P, SSR, T*max* and T*min*, the regional averages of the SD variation were 0.71%, −4.12%, −3.22%, and −2.95%, respectively, while those of the SCD variation were 1.16%, −3.69%, −3.45%, and −4.01%, respectively. Comparatively, SD was more sensitive to SSR, while SCD was more sensitive to air temperature on the SGP. These findings may be helpful to the awareness of snow hydrology and promote its quantitative analysis in the alpine water source areas of large river basins.

**Author Contributions:** L.W.: data collection, methodology, writing—original draft. C.L.: supervision, formal analysis, conceptualization, writing—review and editing. X.X.: data analysis. S.Z., J.L., X.Z. and N.S.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the Key Research Programs of Gansu Province in Science and Technology (20ZD7FA005, 21ZD4FA008), the Open Foundation of MOE Key Laboratory of Western China's Environmental System in Lanzhou University as the Fundamental Research Funds for the Central Universities (lzujbky-2020-kb01).

**Data Availability Statement:** The long-term series of the daily snow depth dataset in China are available in the National Tibetan Plateau data center. These data were derived from the flowing resources available in the public domain: http://data.tpdc.ac.cn. The MODIS Daily Cloudless 500 m Snow Area Product Dataset over China are available in the National Cryosphere Desert Data Center. These data were derived from the flowing resources available in the public domain: http://www.ncdc.ac.cn. The surface net solar radiation data were released by the European Centre for Medium-Range Weather Forecasts (ECMWF) (https://www.ecmwf.int/). The meteorological data were from the National Meteorological Information Center of China Meteorological Administration (http://data.cma.cn/ all accessed on 7 June 2022).

**Acknowledgments:** We are grateful to the National Meteorological Science Data Center, National Tibetan Plateau Data Center, National Cryosphere Desert Data Center, NASA, and the ECMWF for providing open access to data collections and archives.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.
