This study assesses the potential of a hierarchical space-time model for monthly low-flow prediction in Austria. The model decomposes the monthly low-flows into a mean field and a residual field, where the mean field estimates the seasonal low-flow regime augmented by a long-term trend component. We compare four statistical (learning) approaches for the mean field, and three geostatistical methods for the residual field. All model combinations are evaluated using a hydrological diverse dataset of 260 stations in Austria, covering summer, winter, and mixed regimes. Model validation is performed by a nested 10-fold cross-validation. The best model for monthly low-flow prediction is a combination of a model-based boosting approach for the mean field and topkriging for the residual field. This model reaches a median R2 of 0.73. Model performance is generally higher for stations with a winter regime (best model yields median R2 of 0.84) than for summer regimes (R2 = 0.7), and lowest for the mixed regime type (R2 = 0.68). The model appears especially valuable in headwater catchments, where the performance increases from 0.56 (median R2 for simple topkriging routine) to 0.67 for the best model combination. The favorable performance results from the hierarchical model structure that effectively combines different types of information: average low-flow conditions estimated from climate and catchment characteristics, and information of adjacent catchments estimated by spatial correlation. The model is shown to provide robust estimates not only for moderate events, but also for extreme low-flow events where predictions are adjusted based on synchronous local observations.