Soil moisture is a key parameter of the water cycle and energy budget in terrestrial ecosystems.Land data assimilation system can provide spatio-temporally continuous soil moisture data, however, its low spatial resolution limits the further application.Here, the soil moisture output in 0-10 cm soil layer from China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0) was downscaled from 6 km to 1 km in North China by three single models (gradient boosting machine, deep feedforward neural network and random forest) and a Stacking ensemble learning method.The downscaled results for period of April to October in 2019 show that the four downscaling methods can reflect the temporal and spatial variation of soil moisture in North China and somehow alleviate the overestimation of CLDAS products.Both the spatial distribution details and accuracies are improved compared with original CLDAS soil moisture data.Furthermore, the Stacking ensemble learning method outperforms the other three in downscaling performance, including its highest correlation coefficient with observed data (R=0.756 8) and lowest error (RMSE=0.050 5 m³/m³, Bias=-0.005 2 m³/m³).Meanwhile, the downscaled results by Stacking ensemble learning are also highly correlated with the dynamic changes of soil moisture, with lowest RMSE and bias compared with station observations, followed by random forest and deep feedforward neural network.