We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction at more than 200 stream gauge stations in western Canada. We find the novel modelling style to work very well for daily streamflow prediction. Importantly, we interpret model learning to show that it has learned to focus on physically interpretable and physically relevant information, which is a highly desirable quality of machine-learning-based hydrological models.