South Asia is highly vulnerable to hydroclimatic extremes driven by interactions among atmospheric circulation, oceanic variability, and land–atmosphere processes governing monsoon. Improving prediction and risk assessment requires integrated understanding across weather to subseasonal-to-seasonal timescales. This session advances understanding of these drivers and their translation into actionable early-warning systems through physics-based and data-driven approaches. We invite contributions on: (a) large-scale atmospheric–oceanic drivers, including SST variability, jet-stream dynamics, and teleconnections (ENSO, IOD, MJO), shaping monsoon variability and rainfall predictability; (b) observations, reanalysis, remote sensing, and climate model outputs to improve hydroclimatic prediction, including statistical, ML, and hybrid frameworks addressing uncertainty quantification; (c) translation of forecasts into hydrologic impacts, including riverine and urban flooding, inundation mapping, and compound risk assessment; and (d) AI and data-driven early warning frameworks delivering actionable risk communication to communities and stakeholders. This session bridges monsoon dynamics, hydroclimatic extremes, and operational early-warning systems to support interdisciplinary advances.

• A - Atmospheric Sciences
• NH - Natural Hazards