Extreme precipitation events (EPEs) are meteorological phenomena of major concern for society. They can have different characteristics depending on the physical mechanisms responsible for their generation, which in turn depend on the large and mesoscale conditions. This work provides a systematic classification of EPEs over northern–central Italy, one of the regions in Europe with the highest frequency of these events. The EPE statistics have been deduced using the new high‐resolution precipitation dataset ArCIS (Climatological Archive for Central–Northern Italy), that gathers together a very high number of daily, quality‐controlled and homogenized observations from different networks of 11 Italian regions. Gridded precipitation is aggregated over Italian operational warning‐area units (WA). EPEs are defined as events in which daily average precipitation in at least one of the 94 WAs exceeds the 99th percentile with respect to the climate reference 1979–2015. A list of 887 events is compiled, significantly enlarging the database compared to any previous study of EPEs. EPEs are separated into three different dynamical classes: Cat1, events mainly attributable to frontal/orographic uplift; Cat2, events due to frontal uplift with (equilibrium) deep convection embedded; Cat3, events mainly generated by non‐equilibrium deep convection. A preliminary version of this classification is based on fixed thresholds of environmental parameters, but the final version is obtained using a more robust machine‐learning unsupervised K‐means clustering and random forest algorithm. All events are characterized by anomalously high integrated water vapour transport (IVT). This confirms IVT as an important large‐scale predictor, especially for Cat2 events, which is shown to be the most important category in terms of impacts and EPE area extension. Large IVT values are caused by upper‐level waves associated with remotely triggered Rossby wave packets, as shown for two example Cat2 events.

The connection between weather extremes and Rossby wave packets (RWP) has been increasingly documented in recent years. RWP propagation and characteristics can modulate the midlatitude weather, setting the scene for temperature and precipitation extremes and controlling the geographical area affected. Several studies on extreme precipitation events (EPEs) in the Alpine area reported, as the main triggering factor, a meridionally elongated upper‐level trough as part of an incoming Rossby wave packet. In this work, we investigate a wide number of EPEs occurring between 1979 and 2015 in northern‐central Italy. The EPEs are subdivided into three categories (Cat1, Cat2, Cat3) according to thermodynamic conditions over the affected region. It is found that the three categories differ not only in terms of the local meteorological conditions, but also in terms of the evolution and properties of precursor RWPs. These differences cannot be solely explained by the apparent seasonality of the flow; therefore, the relevant physical processes in the RWP propagation of each case are further investigated. In particular, we show that RWPs associated with the strongest EPEs, namely the ones falling in Cat2, undergo a substantial amplification over the western North Atlantic due to anomalous ridge‐building 2 days before the event; arguably due to diabatic heating sources. This type of development induces a downstream trough which is highly effective in focusing water vapour transport toward the main orographic barriers of northern‐central Italy and favouring the occurrence of EPEs.

The ever-increasing complexity and data volumes of numerical weather prediction demand innovations in the analysis and synthesis of operational forecast data. Here we show how dynamical thinking can offer directly applicable forecast information, taking as a case study the extreme northern Italy flooding of May 2023. We compare this event with long-lasting historical northern Italy rainfall events in order to determine (a) why it was so extreme, (b) how well it was predicted, and (c) how we may improve our predictions of such extremes. Lagrangian analysis shows, in line with previous work, that 48-hourly extreme rainfall in Italy can be caused by moist air masses originating from the North Atlantic; North Africa; and, to a lesser extent, eastern Europe, with compounding moisture contributions from all three regions driving the May 2023 event. We identify the large-scale precursors of typical northern Italy rainfall extremes based on geopotential height and integrated vapour transport fields. We show in European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecasts that a precursor perspective was able to identify the growing possibility of the Emilia-Romagna extreme event 8 d beforehand – 4 d earlier than the direct precipitation forecast. Such dynamical precursors prove to be well suited for identifying and interpreting predictability barriers and could help build forecasters' understanding of unfolding extreme scenarios in the medium range. We conclude by discussing the broader implications and operational potential of dynamically rooted metrics for understanding and predicting extreme events, both in retrospect and in real time.

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 from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines 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 are limited, 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 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.

Extreme precipitation events (EPEs) are meteorological phenomena of major concern for the densely populated regions of northern and central Italy. Although statistically rare, they tend to be recurrent in autumn and share common characteristics in the large-scale dynamical evolution responsible for their generation. Past studies on EPEs have reported, as the main triggering factor, a meridionally elongated upper-level trough embedded in an incoming Rossby wave packet. In this respect, we show how the meteorological conditions leading to the devastating 1994 Piedmont flood represent a typical flow evolution for this type of extreme events. Exploiting the systematic classification of EPEs recently published by the authors and taking advantage of a new observational dataset, this article revisits the role of the large-scale flow on this and similar cases of past EPEs.

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 postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising 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 time‐scales. 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 European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast 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.