Antonio Giordani

Floods are among the most damaging hydro-meteorological hazards in the Mediterranean area, where their severity depends not only on rainfall intensity but also on antecedent land-surface conditions and large-scale atmospheric circulation. Emilia-Romagna (Italy) has emerged as a regional flood hotspot, with several devastating events in 2023-2024. In this context, purely univariate perspectives show clear limitations in capturing the complex interactions among meteorological and land-surface drivers, motivating the increasing adoption of compound-event frameworks for the analysis of high-impact floods. This study aims to characterize the compound nature of flood events by disentangling the relative roles of antecedent soil moisture, extreme precipitation, and large-scale atmospheric circulation in driving severe hydrological responses. To this end, we combine ERA5-Land reanalysis data, EFAS river discharge records, and ERA5 large-scale circulation fields over the period 1979-2024. A percentile-based compound-event framework is employed to identify flood-prone preconditioned events - defined as extreme precipitation occurring over anomalously wet soils - and to assess their associated meteorological drivers and hydrological impacts. We implement an Event Coincidence Analysis to quantify how often high levels of soil moisture precede extreme precipitation beyond what is expected by chance, finding coincidence rates over Italy that approach 60\% in winter. Separating heavy rainfall occurrences based on the antecedent soil moisture conditions over the region different signatures emerge: on the one hand, non-preconditioned events, developing over drier catchments, feature more isolated rainfall peaks, and generate weaker, more localized hydrological impacts. On the other hand, preconditioned floods, by contrast, are associated with long-lasting soil moisture anomalies, prolonged rainfall, and persistent large-scale circulation patterns, resulting in markedly stronger and more widespread river-discharge responses. Overall, the results demonstrate that soil moisture memory and atmospheric persistence strongly modulate flood severity in northern Italy. The proposed framework provides a physically-consistent basis for improving the understanding and prediction of high-impact floods across the Mediterranean basin and other hydro-climatically similar regions.

Extreme weather and climate events - such as floods - are among the most impactful hazards for both human society and natural systems. Their accurate representation and prediction remain challenging, especially when such events are driven by interacting climatic factors that operate on different spatial and temporal scales. In this context, large-scale atmospheric circulation patterns and associated teleconnections play a crucial role in modulating regional hydro-meteorological extremes, offering a potential source of predictability that is still not fully exploited or understood. This study examines extreme flood events in Emilia-Romagna, a flood-prone region of northern Italy, with a particular focus on the link between local flood dynamics and broader atmospheric variability.Floods are here categorised into two types: preconditioned floods, which occur when extreme precipitation is preceded by abnormally high levels of soil moisture, and non-preconditioned floods, in which no such antecedent conditions are present. To detect these extremes, a peak-over-threshold method is applied to ERA5-Land reanalysis data spanning the period from 1979 to 2024, capturing significant anomalies in both precipitation and soil moisture. The role of atmospheric low-frequency variability is explored through the analysis of geopotential height anomalies at 500 hPa, derived from ERA5: results indicate that preconditioned flood events are frequently associated with persistent and more intense negative geopotential height anomalies near the affected region, particularly during the winter months, highlighting the role of seasonal atmospheric circulation patterns in enhancing flood risk. Additionally, hydrological impacts are assessed using EFAS river discharge data (1992–2024), showing that preconditioned events are associated with widespread and elevated discharge levels across Emilia-Romagna. Finally, an event coincidence analysis reveals that soil moisture preconditioning is more frequent during winter months, consistent with enhanced synoptic-scale persistence during this season.Overall, the study offers new insight into how persistent large-scale circulation anomalies modulate regional flood risk through both dynamic and hydrological pathways. These findings underscore the importance of incorporating atmospheric teleconnections into flood early-warning systems, and suggest avenues for improving predictive capabilities not only in Emilia-Romagna but also in other areas with comparable climatic and hydrological settings.

Assessing the frequency and impact of large-scale flood events is crucial for improving risk preparedness and emergency management. This effort, however, places emphasis on the need for large data samples to derive statistically robust probabilistic risk estimates. Recent studies have demonstrated the potential of using ensemble climate simulations to drastically extend the sample size of extreme events with respect to the relatively short historical record. This approach has become known as UNSEEN (UNprecedented Simulated Extremes using ENsembles) and has been successfully applied to different types of hazards. In this study, we use ensemble simulations from Copernicus C3S – specifically, seasonal reforecasts from the European Flood Awareness System (EFAS) and ECMWF SEAS5 – to generate a catalogue of flood events that are unprecedented in the historical record, yet plausible in the current climate. We focus on two target regions with recent episodes of severe flooding: The Panaro river basin in northern Italy and the Turia river basin in eastern Spain. We then employ the probabilistic event-based impact model CLIMADA (Aznar-Siguan et al. 2024) to rank the UNSEEN flood events in both basins by their estimated socio-economic impact and develop storylines of unprecedented flood risk. We find that high-impact UNSEEN events generally display recurrent synoptic patterns, similar to those of historical extremes but with higher intensity. Our results suggest that seasonal reforecasts by C3S simulate realistic extremes with respect to historical data following minimal bias correction. This framework can be leveraged to explore risk storylines for stress-testing, and to support the development of mitigation and adaptation strategies to manage disasters.

The increasing frequency and devastating impacts of compound hydro-meteorological extreme events underscore the urgent need for a deeper understanding of their dynamics and occurrence. Compound events involve the simultaneous or sequential occurrence of multiple natural hazards or drivers which may exacerbate impacts compared to individual events alone. Recent striking cases is the exceptional sequence of heavy rainfalls that struck northern Italy in 2023-2024, culminating in widespread flooding across the Emilia Romagna region. The severity and extent of the floods were amplified by antecedent precipitation, which saturated the soil, a pre-condition that resulted in substantial aggravation of the impacts. However, the rarity and unprecedented nature of such events pose major challenges for their robust characterization using observational records alone due to the limited temporal coverage, which can introduce substantial uncertainties. To address this, the UNSEEN (Unprecedented Simulated Extremes using ENsembles) approach has emerged as a powerful method, leveraging large ensembles of seasonal re-forecasts from numerical weather prediction models. By pooling ensemble members, this approach effectively generates surrogate time series spanning thousands of years, enabling the exploration of low-probability, high-impact events within a statistically robust framework. This study applies the UNSEEN methodology to compound flood events within a multivariate framework, focusing on the coupling between precipitation and soil moisture as a key preconditioning driver. The seasonal re-forecasts from SEAS5 (ECMWF) dataset for the period 1994-2023 are considered to characterize unprecedented compound extreme severe floodings in northern Italy, with particular focus on the Emilia-Romagna region. The ability of the UNSEEN ensemble to represent univariate extremes is firstly evaluated—an essential preliminary step to ensure the reliability of the pooled surrogate time series. Subsequently, an event coincidence analysis is conducted on the surrogate series of precipitation-soil moisture extremes to investigate the dominant spatio-temporal patterns arising from their interaction. Results show that the UNSEEN ensemble realistically captures the occurrence of historical extreme flood events, offering a more robust representation compared to observational climatologies alone. These findings underscore the potential of ensemble-based modeling approaches to improve our understanding of rare compound events and support the development of more effective adaptation and mitigation strategies in flood-prone regions.

The urgency for deeper understanding compound hydro-meteorological extreme events is growing, as these events are increasingly recognized for their potential to exacerbate impacts compared to single hazards. Characterized by the concomitance of multiple natural hazards or drivers, compound events are further intensified by climate change, which influences their severity and increases their frequency of occurrence. These hydro-meteorological extremes pose a significant risk to terrestrial ecosystems and have devastating consequences for socio-territorial systems. In Italy, recent extreme events have highlighted this threat, as demonstrated by the unprecedented sequence of heavy precipitation events in 2023-2024 that led to widespread flooding in the region of Emilia Romagna in central-northern Italy. These low-probability events, which resulted in several fatalities and damages amounting to tens of billions of euros, were amplified by antecedent precipitation that saturated soils, significantly enhancing the runoff response and, consequently, flood severity and extension. Indeed, the pre-condition given by soil imbibition preceding heavy rainfall occurrences is crucial in determining the potential severity of the event, but its comprehensive understanding is still limited.This study investigates the relationship between precipitation and soil moisture conditions in Italy, with the goal to quantitatively characterize their role in the occurrence of historical and plausible compound hydro-meteorological extremes. We employ state-of-the-art reanalysis datasets (ERA5 and ERA5-Land) to analyze a series of representative extreme precipitation events, focusing on their antecedent soil moisture conditions and estimating the typical temporal scales of the associated co-variation. The link between these hydro-meteorological quantities and riverine flood occurrences is assessed considering streamflow discharge data from EFAS hydrological reanalysis dataset. The prevailing large-scale conditions driving these events, in terms of the 500-hPa geopotential height and the integrated water vapor transport column, are explored to identify the key dynamical features responsible for the occurrence of compound flooding. Additionally, a large ensemble of seasonal numerical weather forecasts is employed to sample the phase space of precipitation-soil moisture conditions applying the so-called UNSEEN (Unprecedented Simulated Extremes using ENsembles) approach. Within this framework, the probability of compound precipitation-soil moisture extremes is assessed through a statistical event coincidence analysis to understand the dominant spatio-temporal patterns of their interaction; moreover, physical storylines of rare, yet plausible, extreme flood events will be built through ensemble pooling.

This study focuses on the validation of high-resolution regional reanalyses to understand their effectiveness in reproducing precipitation patterns over Italy, a climate change hotspot characterized by coastal sea-land interaction and complex orography. Nine reanalysis products were evaluated, with the ECMWF global reanalysis ERA5 serving as a benchmark. These included both European (COSMO-REA6, CERRA) and Italy-specific (BOLAM, MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA\_IT) datasets, using different models and parametrizations. The inter-comparison involved determining the effective resolution of daily precipitation fields using wavelet techniques and assessing intense precipitation statistics through frequency distributions. In-situ observations and observational gridded datasets were used to independently validate reanalysis precipitation fields. The capability of reanalyses to depict daily precipitation patterns was assessed, highlighting a maximum radius of precipitation misplacement of about 15 km, with notably lower skills during summer. An overall overestimation of precipitation was identified in the reanalysis climatological fields over the Po Valley and the Alps, whereas multiple products showed an underestimation of precipitations across the North-West coast, the Apennines, and Southern Italy. Finally, a comparison with a time-consistent observational dataset (UniMi/ISAC-CNR) revealed a non-stable deviation from observations in the annual precipitation cumulate of the reanalysis products analyzed. This should be taken into account when interpreting precipitation trends over Italy.

The challenges associated with reliably observing and simulating hazardous hailstorms call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modelling, to improve understanding of where and when severe hail most often occurs. In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting High rEsolution ReAnalysis over Italy (SPHERA) reanalysis predictors describing hail-favourable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data filtering. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing-level height. A hail frequency estimate over the extended summer season (April–October) in south-central Europe is presented for a test period of 5 years (2016–2020). OT-derived hail frequency peaks at around 15:00 UTC in June–July over the pre-Alpine regions and the northern Adriatic Sea. The hail proxy statistically matches with ∼63 % of confirmed ESWD reports, which is roughly 23 % more than the previous estimate over Europe coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights the enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstone diameters ≥ 3 cm). Further, signatures for missed small-hail occurrences are identified, which are characterized by lower instability and organization and warmer cloud top temperatures.

Among severe weather events related to deep moist convection, hail represents one of the most hazardous perils for terrestrial ecosystems and human-related activities such as agriculture and building and insurance sectors. Furthermore, the severity and frequency of high-impact hailstorms is increasing throughout Europe, with the highest potential to worsen expected over northern Italy. However, multiple limitations hinder a comprehensive understanding of where and when severe hail can occur. Indeed, the scarcity and incompleteness of information provided by the available observations, and the approximate representations possible with numerical simulations limit a thorough assessment of hail occurrence. These challenges call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modeling. In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting SPHERA reanalysis predictors describing hail-favorable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data selection. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter built to sub-sample the OTs, namely: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing level height. A hail frequency estimate over the extended summer season (April-October) in south-central Europe is presented for a test period of 5 years (2016-2020). OT-derived hail frequency peaks at around 15 UTC in June-July over Italian pre-Alpine regions and the northern Adriatic sea. The hail proxy statistically matches with ∼63% of confirmed ESWD reports, which is roughly 23% more than the previous estimate over Europe obtained by coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstones diameters ≥ 3 cm). Further, signatures for small-hail missed occurrences are identified, which are characterized by lower instability and organization, and warmer cloud-top temperatures.

The interest towards the development of regional high-resolution retrospective datasets, allowing an enhanced representation of past meteorological states, has been progressively increasing. Convection-permitting (CP) datasets (i.e., hindcasts, based on historical model integrations, or reanalyses, including the additional assimilation of historical observational data) have demonstrated to improve the representation of precipitation compared to convection-parameterized counterparts. The benefits involve the spatial structures of rainfall fields, the timing and peak of the diurnal cycle of summer precipitation, and the frequency of wet days/hours. This is of particular relevance for enhancing the characterization of severe precipitation events with the aim to prevent and minimize their impacts on terrestrial ecosystems, and on human and animal life. However, the simulation of convective-related phenomena is highly model-dependent, implying the inability to sample the full range of natural variability with single-model experiments. This is exacerbated for km-scale simulations owing to the intrinsic chaotic behavior underlying convective processes. Recently, the development of Multi-Model Ensembles (MMEs) of CP regional climate models over Europe has demonstrated to efficiently tackle this issue and reduce the simulation error associated with single model outputs. This approach could benefit also retrospective estimates in order to retrieve a complete, homogeneous, and optimized assessment of past atmospheric states. In case of precipitation, this could be valuable also for potential downstream modeling applications such as forcing hydrological forecasting systems to obtain improved historical series of high-impact flood events. This work presents the first MME of retrospective CP datasets over Italy based on four reanalyses/hindcasts recently produced, with the aim to assess the added value of their joint employment. The datasets are obtained by dynamically downscaling the global reanalysis ERA5 using different numerical models: MERIDA-HRES (based on WRF-ARW), the hindcast based on the model MOLOCH, and SPHERA and VHR_REA-IT (both based on COSMO). The reference dataset for comparison is GRIPHO, the first Italian pluviometer-based hourly analysis. The investigation over a decade (2007-2016) includes various aspects such as the annual and seasonal dependence of daily and hourly mean rainfall intensity and frequency, heavy precipitation occurrences, and their summer diurnal cycles. The results indicate that a superior dataset performing always best is not detected, while large inter-model variability characterizes summer precipitation. The ensemble aggregation systematically improves rainfall estimates over single datasets, resulting in more adherent spatial fields and lower root-mean-squared errors and relative biases when compared to the observations, at the expense of reduced spatial variability of the distributions.

The challenges associated with reliably observing and simulating hazardous hailstorms call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modeling, to improve understanding of where and when severe hail most often occurs. In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting SPHERA reanalysis predictors describing hail-favorable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data filtering. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter, namely: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing level height. A hail frequency estimate over the extended summer season (April–October) in south-central Europe is presented for a test period of 5 years (2016–2020). OT-derived hail frequency peaks at around 15 UTC in June–July over the pre-Alpine regions and the northern Adriatic sea. The hail proxy statistically matches with ∼62 % of confirmed ESWD reports, which is roughly 22 % more than the previous estimate over Europe coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstones diameters ≥ 3 cm). Further, signatures for small-hail missed occurrences are identified, which are characterized by lower instability and organization, and warmer cloud-top temperatures.