Municipal water withdrawal (MWW) information is of great significance for water supply planning, including water supply pipeline networks planning, optimization and management. Currently most MWW data are reported as spatially aggregated over large-area survey regions or even lack of data, which is unable to meet the growing demand for spatially detailed data in many applications. In this paper, six different models are constructed and evaluated in estimating global MWW using aggregated MWW data and gridded raster covariates. Among the models, the artificial neural network-based indirect model (NNM) shows the best accuracy with higher R2 and lower NMAE and NRMSE in different spatial scales. The estimates achieved from the NNM model are consistent with census and survey data, and outperforms the existing global gridded MWW dataset. At last, the NNM model is applied to mapping global gridded MWW for the year 2015 at 0.1 × 0.1° resolution. The proposed method can be applied to a wider aggregated output learning problem and the high-resolution global gridded MWW data can be used in hydrological models and water resources management.