**5. Conclusions**

This study investigated and developed a new downscaling methodology, such as GMWPA at 0.05◦ resolution based on the multitemporal GPM precipitation dataset (2001 to 2015) at 0.1◦ and ASTER 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in EDBF algorithm. The proposed methodology is a two-stepped process: (i) to develop a scale dependent relationship between precipitation variables, i.e., the multitemporal GPM precipitation and the weighted precipitation, and geospatial predictors through regression analysis [45]; (ii) the downscaling of EDBF-based multitemporal weighted precipitation at a refined scale. In addition, EDBF results were validated using neutral variables, e.g., the GPM-based annual 2006 and 2012 precipitation, the TRMM-based annual (2001, 2006 and 2012) and the average annual (2001–2015) precipitation. The following conclusions are drawn from this work:

• Geospatial predictors were the proxy of precipitation and polynomial function best described the relationship between the multitemporal precipitation variables and geospatial predictors, i.e., elevation, longitude, and latitude.


In conclusion, it is possible to accurately downscale the GPM-based multitemporal precipitation using geospatial predictors in the humid region (Southern China) of Mainland China and that the presented methodology is generic in nature and is applicable in all climatic conditions of the world.

*Remote Sens.* **2020**, *12*, 3162

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-4292/12/19/3162/s1, Figure S1: Grids conversion into points located at the center of each pixel (a) 0.25◦ resolution, (b) 0.50◦ resolution, (c) 0.75◦ resolution, (d) 1.0◦ resolution, (e) 1.25◦ resolution, (f) 1.50◦ resolution, respectively; Figure S2: Execution of EDBF algorithm for estimating the weighted precipitation at different scaled resolutions; Figure S3: Per meter elevation received precipitation at 0.25◦, 0.50◦, 0.75◦, 1.0◦, 1.25◦ and 1.50◦ resolution for (a) the average monthly, (b) the average annual (2001–2015), (c) the average winter, (d) the average spring, (e) the average summer, (f) the average autumn, (g) the dry-year (2001), (h) the wet-year (2004) precipitation, respectively; Figure S4: Per degree longitude received precipitation at 0.25◦, 0.50◦, 0.75◦, 1.0◦, 1.25◦ and 1.50◦ resolution for (a) the average monthly, (b) the average annual (2001–2015), (c) the average winter, (d) the average spring, (e) the average summer, (f) the average autumn, (g) the dry-year (2001), (h) the wet-year (2004) precipitation, respectively; Figure S5: Per degree latitude received precipitation at 0.25◦, 0.50◦, 0.75◦, 1.0◦, 1.25◦ and 1.50◦ resolution for (a) the average monthly, (b) the average annual (2001–2015), (c) the average winter, (d) the average spring, (e) the average summer, (f) the average autumn, (g) the dry-year (2001), (h) the wet-year (2004) precipitation, respectively; Figure S6: Generation of the high-resolution weightged residuals (g)(h)(i) at 0.05◦ from the low-resolution weighted residuals (d–f) at 0.75◦ for, (a) the dry yar (2001), (b) the wet year (2005), and (c) the average annual (2001–2015) precipitatation at 0.75◦ resolution, respectively. Table S1: Data Summary for the calculation of Chi-square test value; Table S2: Comparison between the weighted precipitation and the multitemporal precipitation variables at different resolution scales.

**Author Contributions:** Conceptualization, S.U.; methodology, S.U.; software, Z.Z. and S.U.; validation, I.I. and J.Z.; formal analysis, S.H.; investigation, Y.L.; resources, J.Z. and F.Z.; data curation, Y.S. and M.Y.; writing—original draft preparation, S.U. and Z.Z.; writing—review and editing, S.U.; visualization, S.U. and Z.Z.; supervision, F.Z. and S.H.; project administration, L.Y.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project was funded by National Key R & D Program of China, No. 2017YFB0503003, and Guizhou Province Project (China) for the Collaborative Innovation Center for Karst Mountain Ecological Environment Protection and Resource Utilization.

**Acknowledgments:** We really thanks to National Aeronautics and Scientific Administration (NASA) Earth Science GESDISC Data Archive for provision of free online data.

**Conflicts of Interest:** All authors declare no conflict of interest.
