Depth to bedrock serves as the lower boundary of soil, which influences or controls many of the Earth’s physical and chemical processes. It plays important roles in geology, hydrology, land surface processes, civil engineering, and other related fields. This paper describes the materials and methods to produce a high-resolution (100 m) depth-to-bedrock map of China. Observations were interpreted from borehole log data (ca. 6,382 locations) sampled from the Chinese National Important Geological Borehole Database. To fill in large sampling gaps, additional pseudo-observations generated based on expert knowledge were added. Then, we overlaid the training points on a stack of 133 covariates including climatic images, DEM-derived parameters, land-cover and land-use maps, MODIS surface reflectance bands, vegetation index images, and the Harmonized World Soil Database. Spatial prediction models were developed using the random forests and gradient boosting tree, and ensemble prediction results were then obtained by these two independently fitted models. Finally, uncertainty estimation was generated by the quantile regression forest model. The 10-fold cross-validation showed that the ensemble models explain 57 % of the variation in depth to bedrock. Based on comparison with depth-to-bedrock maps of China extracted from previous global predictions, our predictions showed higher accuracy. More observations, especially those in data-sparse areas, should be added to training data, and more covariates with high precision should be used to further improve the accuracy of spatial predictions. The resulting maps of this study are available on Figshare at the following DOI: https://doi.org/10.6084/m9.figshare.7011524.v1 . And they are also available for download at http://globalchange.bnu.edu.cn/ .