Droughts pose escalating threats to global water security, agriculture, and socioeconomic stability amid anthropogenic climate change, with projections indicating an increase in frequency, duration, and severity driven by altered precipitation patterns and amplified evaporative demand. This study introduces a probabilistic framework to quantify drought risk amplification, employing the Risk Ratio (RR) methodology integrated with extreme value theory and non-parametric inference. Utilizing multi-model ensemble (MME) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), we evaluate changes in drought characteristics-duration, frequency, and severity - via the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-and 12-month timescales for near-future (NF) and far-future (FF) periods. Our analyses reveal pervasive global intensification, with over 90% of land grids exhibiting positive severity shifts under SSP5-8.5 in the FF, attributed to atmospheric evaporative demand, which accounts for approximately 44% of the trends in SPEI. Threshold-stratified RR assessments reveal nonlinear escalations at higher percentiles (P90 vs. P75), compressing the return periods of extreme events by 20%-30% under high-emission scenarios. Regional hot-spots, including the Amazon basin, sub-Saharan Africa, southwestern North America, and Central Asian drylands, exhibit frequency risks that are 4-fold or more amplified, signaling transitions to chronic water stress and potential ecosystem tipping points. These findings underscore the dominance of thermodynamic drivers in drought dynamics, advocating for emissions mitigation to curtail risks by 15%-25% under moderate pathways. By addressing uncertainties in non-stationary regimes, this framework provides adaptive strategies for resilient water management, offering policymakers critical insights to mitigate cascading impacts on global food security and biodiversity in a warming world. (c) 2025 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).