GRACE satellite has opened a new era of quantitative retrieval of groundwater change by remote sensing， but it has the problem of low spatial resolution. High-resolution groundwater observations will significantly improve the accuracy of local-scale hydrological process understanding， thereby offering essential data support for the development of scientifically based groundwater management policies. After sorting out the characteristic factors such as precipitation， air temperature， evapotranspiration， surface temperature， normalized vegetation index and soil water in the Yellow River Basin， partial least squares regression method was used to screen the characteristic factors respectively from January to December， and the optimal monthly characteristic factor subset was constructed. Then， the random forest algorithm was used to downscale the groundwater data of the Yellow River Basin from 0.25°× 0.25° to 1 km×1 km， and compared and verified with the measured groundwater level data. The results show that： （1） Except evapotranspiration and surface temperature， the importance of other factors changes with the change of month； （2） In the time series， the correlation coefficient and Nash coefficient of groundwater data before and after downscaling are as high as 0.95， and the root-mean-square error is 3.17 mm； （3） Spatially， compared with before downscaling， the correlation coefficient between the change data of groundwater reserves and the measured groundwater level after downscaling increased by 47.67%. The research results can meet the demand for high-resolution groundwater data in practical applications， and provide reference for the feature factor screening of groundwater downscaling research.