High-resolution geoscience data are essential for understanding and predicting extreme weather events, yet producing such data remains a major challenge due to limitations in observational infrastructure and computational cost. This project introduces a transformative AI-based framework to overcome these barriers by generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. These enhanced datasets will empower better decision-making in disaster preparedness, emergency response, and infrastructure planning. The project’s broader societal impacts include advancing tools for tropical cyclone prediction and wildfire detection, training a new generation of interdisciplinary scientists in AI and geosciences, and releasing open-source software for broad accessibility. By integrating explainable AI with physical principles and expert knowledge, the research aims to improve public trust in scientific models and provide actionable insights for meteorologists, policymakers, and emergency managers. Outreach efforts and mentoring initiatives will promote participation in STEM and foster the development of future leaders in climate resilience and AI for natural hazards. This project develops a next-generation generative downscaling framework that combines diffusion-based generative models with physical constraints, domain expertise, and probabilistic uncertainty quantification. Key innovations include physics-guided loss functions to enforce geophysical