Global Climate Models （GCMs） are the primary tools currently used to predict future climate change；however，their coarse spatial resolution limits their ability to assess localized impacts of climate change. To address this issue，statistical downscaling methods based on linear regression equations have been developed to enhance the spatial resolution of GCMs and have continuous improvement and innovation. Meanwhile，machine learning techniques have demonstrated superior performance in various predictive modeling problems， making them potential new tools for statistical downscaling. Therefore，this study applied machine learning model-Light Gradient Boosting Machine（LightGBM）to construct statistical downscaling model for each site，combined with information flow method to select forecasting factors，and compared with linear regression method （stepwise multiple linear regression method based on Empirical Orthogonal Function）to explore the application ability of LightGBM in the field of statistical downscaling. The two methods were applied to downscale the meteorological element of the Yellow River basin，an important climate change sensitive area in China，establishing statistical downscaling models for 90 stations within the basin to generate temperature and precipitation data for the summer months（June，July，August）from 1965 to 2014. The performance of both methods is evaluated through an analysis of the correlation coefficients，root mean square errors（RMSE），and spatial distributions be‐ tween downscaled values and observed values. The results show that both downscaling methods can correct the temperature error of the reanalysis data（ERA5）in the northern part of the basin. LightGBM shows superior inter-site correlation，but 60，64，52 sites show higher RMSE than regression method in June，July，and August， respectively. For precipitation downscaling，neither of the two downscaling datasets nor ERA5 could accurately represent the spatial distribution of observed values，but the downscaling value obtained by LightGBM had a higher inter-site correlation coefficient than the regression method，and only 16，7，14 sites showed higher RMSE than the regression method in June，July and August. Considering the potential of machine learning methods for modeling nonlinear problems，it is still necessary to further improve the algorithm and improve the quality of downscaling data sets in the future. The advantages and disadvantages of machine learning in downscaling work provided a technical reference and support for using statistical downscaling methods to generate high-resolution temperature and precipitation data in the future.