  A number of studies have investigated the large-scale drivers and
upstream-precursors of extreme weather events, making it clear that the
earliest warning signs of extreme events can be remote in both time and space
from the impacted region. Integrating and leveraging our understanding of
dynamical precursors provides a new perspective on ensemble forecasting for
extreme events, focused on building story-lines of possible event evolution.
This then acts as a tool for raising awareness of the conditions conducive to
high-impact weather, and providing early warning of their possible development.
However, operational applications of this developing knowledge-base is limited
so far, perhaps for want of a clear framework for doing so. Here, we present
such a framework, supported by open software tools, designed for identifying
large-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 by
systematically analysing the precursor circulations of daily rainfall extremes
across 18 regional- to national-scale European domains. We discuss the
precursor rainfall dynamics for three disparate regions, and show our findings
are consistent with, and extend, previous work. We provide an estimate of the
predictive utility of these precursors across Europe based on logistic
regression, and show that large-scale precursors can usefully predict heavy
rainfall between two and six days ahead, depending on region and season. We
further show how for more continental-scale applications the
regionally-specific precursors can be synthesised into a minimal set of indices
that drive heavy precipitation. We then provide comments and guidance for
generalisation and application of our demonstrated approach to new variables,
timescales and regions.
