PREEVENTS Track 2: Collaborative Research: Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United States

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PREEVENTS Track 2: Collaborative Research: Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United StatesExtreme precipitation is a natural hazard that poses risks to life, society, and the economy. Impacts include mortality and morbidity from fast-moving water, contaminated water supplies, and waterborne diseases as well as dam failures, power and transportation disruption, severe erosion, and damage to both natural and agro-ecosystems. These impacts span several sectors including water resource management, energy, infrastructure, transportation, health and safety, and agriculture. However, on the timeframe required by many decision makers for planning, preparing, and resilience-building "subseasonal to seasonal (S2S; 14 to 90 days)" forecasts have poor skill and thus adequate tools for prediction do not exist. Additionally, societal resilience to these events cannot be increased without established, two-way communication pathways between researchers, forecasters, and local or regional decision makers. Therefore, the goal of this project is to enhance scientific understanding of S2S extreme precipitation events, improve their prediction, and increase communication between research and stakeholder communities with regard to such events. The overarching results will be the development of predictive models that have the potential to reduce mortality, morbidity, and damages caused by S2S extreme precipitation events and broadening participation in science by including federal, tribal, and local stakeholders. Three targeted user communities; water resource managers, emergency managers, and tribal environmental professionals will engage throughout the project duration via workshops. The co-production of knowledge will steer the science to focus on useful characteristics that matter most to the people who use and rely on predictions, thus contributing to knowledge-sharing and improving the capability to predict what is meaningful.This project will enhance fundamental understanding of the large-scale dynamics and forcing of S2S extreme precipitation events in the U.S. and improve capability to model and predict such events. This project assembles an expert team of scientists and stakeholders to narrow the prediction gap of S2S extreme precipitation events by answering four scientifically and societally relevant research questions: 1) What are the synoptic patterns associated with, and characteristics of, S2S extreme precipitation events in the contiguous U.S.? 2) Do large-scale modes of climate variability modulate these events? If so, how? 3) How predictable are S2S extreme precipitation events across temporal scales? and 4) How do we create an informative prediction of S2S extreme precipitation events optimized for policymaking and planning? To answer these questions, this project will for the first time, combine observations with novel machine-learning techniques, high-resolution radar composites, dynamical climate models (the National Multi-Model Ensemble and the Coupled Model Intercomparison Project phase 5), and workshops that engage stakeholders in the co-production of knowledge. This project will identify the fundamental weather and climate processes that are tied to S2S extreme precipitation events across the U.S. from scales as small as individual storms to those as large as ocean basins. The prediction skill for S2S extreme precipitation events will be improved through an increased mechanistic understanding of historical events and a quantitative evaluation of model performance for simulating these events and their characteristic patterns. The statistical and co-production frameworks developed in this project will have the flexibility to be applied across meteorological extremes and timescales, in other global regions, with future climate model simulations, and with other stakeholder communities to reduce the impact of and increase resilience to extreme meteorological events.