As coastal communities worldwide face growing exposure to flooding risks, providing prediction products across subseasonal-to-interannual timescales becomes more urgent. Meeting this demand requires advancing understanding of how local processes, including tides, storm surge, riverine flooding, and coastally-trapped waves, interact with large-scale climate drivers and rising seas to govern predictability. It also requires bridging the divide from large-scale climate model output to the sub-kilometer scales needed for community-level planning. This session seeks to identify research gaps limiting operational extended-range coastal flood prediction and explore approaches to meeting stakeholder needs. Papers are invited on the prediction and predictability of coastal flooding risks for lead times ranging from two weeks to two years, including contributions on probabilistic forecasting approaches in numerical and statistical models, methods to identify processes and/or phenomena providing high potential skill, new downscaling and postprocessing techniques including machine learning, and end-user perspectives on information uptake and translation needed for planning.

• GC - Global Environmental Change
• NH - Natural Hazards