Climate variability and change is increasing the frequency and severity of droughts, floods, and heatwaves, creating major challenges for agricultural production, especially in water-limited regions or areas vulnerable to excess water. These impacts extend beyond crops to animal-based food systems, where extremes reduce yield, crop quality, and disrupt livestock productivity. Links among food, water, and energy systems further intensify these risks.

Process-based and statistical models have long addressed these challenges. Advances in AI/ machine learning (ML) offer new opportunities to enhance prediction and decision-making at regional to global scales.

This session invites contributions applying AI/ML approaches integrated with process-based models to advance characterization, prediction, and risk management of weather and climate extremes. We welcome studies on compound events, early warning systems, subseasonal-to-seasonal prediction, and multi-source data integration, including remote sensing. We particularly encourage contributions linking modeling advances to actionable outcomes for agriculture, water resources, and rangeland sustainability.

• B - Biogeosciences
• H - Hydrology
• NH - Natural Hazards
• SY - Science and Society