Global water models allow us to explore the terrestrial water cycle in earth-sized digital laboratories to support science and guide policy. However, these models are still subject to considerable but also reducible uncertainties that can be attributed to mainly three sources: (1) imbalances in data quality and availability across geographical regions and between hydrologic variables, (2) poorly quantified human influence on the water cycle, and (3) difficulties in tailoring process representations to regionally diverse hydrologic systems. New, more accurate, and larger datasets, as well as better accumulated and even enhanced process knowledge, will help to reduce these uncertainties and thus improve model consistency with our perceptions and accuracy given existing observations. This review examines the sources of uncertainty crucial for global water models and proposes actions to mitigate them, thereby providing a roadmap for model advancement. Following this path will yield more consistent and accurate models that are urgently needed to tackle key scientific and societal challenges.