  In 2021 300 mm of rain, nearly half the average annual rainfall, fell near
Catania (Sicily island, Italy). Such events took place in just a few hours,
with dramatic consequences on the environmental, social, economic, and health
systems of the region. This is the reason why, detecting extreme rainfall
events is a crucial prerequisite for planning actions able to reverse possibly
intensified dramatic future scenarios. In this paper, the Affinity Propagation
algorithm, a clustering algorithm grounded on machine learning, was applied, to
the best of our knowledge, for the first time, to identify excess rain events
in Sicily. This was possible by using a high-frequency, large dataset we
collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily
Extreme dataset). Weather indicators were then been employed to validate the
results, thus confirming the presence of recent anomalous rainfall events in
eastern Sicily. We believe that easy-to-use and multi-modal data science
techniques, such as the one proposed in this study, could give rise to
significant improvements in policy-making for successfully contrasting climate
changes.
