Highlights What are the main findings? Ensemble machine learning was employed to generate multi-year global terrestrial water storage and water withdrawal by integrating remote sensing and geospatial datasets. Big data and IPCC exposure-hazard-vulnerability paradigm were used to assess variation and evolution of global water scarcity risk over the past two decades. What is the implication of the main finding? Largest TWS losses and highest risk cluster in Asia and Africa imply that policy should prioritize storage buffering, withdrawal management and capacity building to curb widening water-security inequities. A storage-aware remote sensing-driven EHV framework offers a consistent basis for global risk mapping, supporting operational early warning and transboundary planning while reducing dependence on model-only proxies.Highlights What are the main findings? Ensemble machine learning was employed to generate multi-year global terrestrial water storage and water withdrawal by integrating remote sensing and geospatial datasets. Big data and IPCC exposure-hazard-vulnerability paradigm were used to assess variation and evolution of global water scarcity risk over the past two decades. What is the implication of the main finding? Largest TWS losses and highest risk cluster in Asia and Africa imply that policy should prioritize storage buffering, withdrawal management and capacity building to curb widening water-security inequities. A storage-aware remote sensing-driven EHV framework offers a consistent basis for global risk mapping, supporting operational early warning and transboundary planning while reducing dependence on model-only proxies.Abstract A storage-aware water-scarcity risk assessment framework coupling satellite remote sensing, geospatial datasets with the IPCC exposure-hazard-vulnerability (EHV) paradigm was designed to evaluate the spatiotemporal dynamics of global water scarcity risk over the past two decades. To achieve this, a performance-weighted ensemble machine learning approach was employed to reconstruct long-term terrestrial water storage (TWS) from satellite observations, augmented with glacier-mass calibration to improve reliability in cryosphere-affected regions. Global water withdrawal dataset was generated by integrating remote sensing, geospatial dataset, and machine learning to mitigate the dependency of parameterized land surface hydrological models and enable consistent risk mapping. Satellite-derived results reveal obvious TWS declines in Asia, Northern Africa, and North America, particularly in irrigated drylands and glacier-dominated regions. EHV paradigm and big datasets further identified high-water scarcity risk in Asia and Africa, especially in agricultural regions. Water stress has intensified in Africa over the past two decades, while a decreasing trend is observed in parts of Asia. Vulnerability levels in Asia and Africa are approximately eight times higher than those in other global regions. Results reveal a strong connection between water stress and socioeconomic factors in Asia and Africa, reflecting global disparities in water resource availability.