Downscaled monthly precipitation dataset at 1 km resolution over Guangxi, China and ASEAN regions from 2001 to 2020

Five types of kernel function are adopted in our GWR model, including the gaussian, exponential, bisquare, tricube, and boxcar kernel function. The optimal kernel function is selected based on the correlation coefficient, the root mean square error and bias. Thus, we established the 1-km annual precipitation data in 2001-2020 for Guangxi, China and the ASEAN regions.

The 1-km monthly precipitation dataset from 2001 to 2020 was also generated by the proportional coefficient method. Moreover, ground observation data from 2679 stations in 2001-2020 were used for verification, and the overall pearson correlation and correlation coefficient were 0.890 and 0.792, respectively. The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations under 1-km resolution in detail, and could be potentially promising for ecological environment, hydrological management, flood prediction and other relevant fields.