The accelerating global hydrological changes demand innovative approaches to understand coupled surface water-groundwater systems. Building on my doctoral research in inter-basin water management under forecast uncertainties, I propose to expand this expertise into a global-scale analysis framework for terrestrial water system variations. My vision integrates machine learning (ML), and explainable AI (XAI) to disentangle climate-human-water interactions across spatial-temporal scales.