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A number of studies have investigated the large-scale drivers andupstream-precursors of extreme weather events, making it clear that theearliest warning signs of extreme events can be remote in both time and spacefrom the impacted region. Integrating and leveraging our understanding ofdynamical precursors provides a new perspective on ensemble forecasting forextreme events, focused on building story-lines of possible event evolution.This then acts as a tool for raising awareness of the conditions conducive tohigh-impact weather, and providing early warning of their possible development.However, operational applications of this developing knowledge-base is limitedso far, perhaps for want of a clear framework for doing so. Here, we presentsuch a framework, supported by open software tools, designed for identifyinglarge-scale precursors of categorical weather events in an automated fashion,and for reducing them to scalar indices suitable for statistical prediction,forecast interpretation, and model validation. We demonstrate this framework bysystematically analysing the precursor circulations of daily rainfall extremesacross 18 regional- to national-scale European domains. We discuss theprecursor rainfall dynamics for three disparate regions, and show our findingsare consistent with, and extend, previous work. We provide an estimate of thepredictive utility of these precursors across Europe based on logisticregression, and show that large-scale precursors can usefully predict heavyrainfall between two and six days ahead, depending on region and season. Wefurther show how for more continental-scale applications theregionally-specific precursors can be synthesised into a minimal set of indicesthat drive heavy precipitation. We then provide comments and guidance forgeneralisation and application of our demonstrated approach to new variables,timescales and regions.

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall