  The accurate prediction of intense precipitation events is one of the main
objectives of operational weather services. This task is even more relevant
nowadays, with the rapid progression of global warming which intensifies these
events. Numerical weather prediction models have improved continuously over
time, providing uncertainty estimation with dynamical ensembles. However,
direct precipitation forecasting is still challenging. Greater availability of
machine learning tools paves the way to a hybrid forecasting approach, with the
optimal combination of physical models, event statistics, and user-oriented
post-processing. Here we describe a specific chain, based on a random forest
pipeline, specialised in recognizing favourable synoptic conditions leading to
precipitation extremes and subsequently classifying extremes into predefined
types. The application focuses on Northern and Central Italy, taken as a
testbed region, but is seamlessly extensible to other regions and timescales.
The system is called MaLCoX (Machine Learning model predicting Conditions for
eXtreme precipitation) and is running daily at the Italian regional weather
service of ARPAE Emilia-Romagna. MalCoX has been trained with the ARCIS gridded
high-resolution precipitation dataset as the target truth, using the last 20
years of the ECMWF re-forecast dataset as input predictors. We show that, with
a long enough training period, the optimal blend of larger-scale information
with direct model output improves the probabilistic forecast accuracy of
extremes in the medium range. In addition, with specific methods, we provide a
useful diagnostic to convey to forecasters the underlying physical storyline
which makes a meteorological event extreme.
