One of the most relevant and debated topics related to the effects of the climate change is whether intense rainfall events have become more frequent over the last decades. It is a crucial aspect, since an increase in the magnitude and frequency of occurrence of heavy rainfall events could result in a dramatic growth of floods and, in turn, human lives losses and economic damages. Because of its central position in the Mediterranean area, Sicily has been often screened with the aim to capture some trends in precipitation, potentially related to climate change. While Mann‐Kendall test has been largely used for the rainfall trend detection, in this work a different procedure is considered. Precipitation trends are here investigated by processing the whole rainfall time‐series, provided by the regional agency SIAS at a 10‐min resolution, through the quantile regression method by aggregating precipitation across a wide spectrum of durations and considering different quantiles. Results show that many rain gauges are characterized by an increasing trend in sub‐hourly precipitation intensity, especially at the highest quantiles, thus suggesting that, from 2002 to 2019, sub‐hourly events have become more intense in most of the island. Moreover, by analysing some spatial patterns, it has been revealed that the south and the east of Sicily are more interested in significant increasing rainfall trends, especially at the 10‐min duration. Finally, the comparison between the two procedures revealed a stronger reliability of the quantile regression in the trend analysis detection, mainly due to the possibility of investigating the temporal variation of the tails of precipitation distribution.

Heavy rainfall events are likely to become more frequent and severe in the next future, under the effects of a changing climate. In this context, the scientific literature is characterized by a relevant number of studies trying to identify the convective component of precipitation, since this kind of phenomena, due to their short duration and high intensity, may lead to an increased risk of flash floods or debris flows and, consequently, human life losses and economic damages. In this study, a separation between the convective and stratiform precipitation, starting from the subhourly precipitation time series recorded over Sicily (Italy), is provided on a monthly scale. Results show that the percentage of convective precipitation increases as the associated intensity increases, following what is generally known about convective events. Moreover, during the summer months, higher percentages of convective rainfall are generally reached for lower values of intensity, since this season favours the occurrence of these phenomena due to high temperature and relative humidity. In addition to the regional analysis, even an at‐site analysis has been carried out, through the definition of a critical intensity threshold to identify the predominant convective component for each rain gauge. This analysis has pointed out that such convective events have occurred mainly in the east part of the island mainly due to the complex orography of this area, which favours the occurrence not only of such phenomena but of flash floods and debris flows as well.

Climate change affects all the components of the hydrological cycle. Starting from precipitation distribution, climate alterations have direct effects on both surface water and groundwater in terms of their quantity and quality. These effects lead to modifications in water availability for agriculture, ecology and other social uses. Change in rainfall patterns also affects the runoff of natural rivers. For this reason, studying runoff data according to classical hydrological approaches, i.e., statistical inference methods that exploit stationary probability distributions, might result in missing important information relevant to climate change. From this point of view, a new approach has to be found in the study of this type of data that allows for non-stationary analysis. In this study, the statistical framework known as Generalized Additive Models for Location, Scale and Shape (GAMLSS), which can be used to carry out non-stationary statistical analyses, was applied in a non-stationary frequency analysis of runoff data collected by four gauges widely distributed across Sicily (Italy) in the period 1916–1998. A classical stationary frequency analysis of these runoff data was followed by a different non-stationary frequency analysis; while the first was made using annual rainfall as a covariate, with the aim of understanding how certain statistical parameters of runoff distribution vary with changes in rainfall, the second derived information about the temporal variability of runoff frequencies by considering time as a covariate. A comparison between stationary and non-stationary approaches was carried out using the Akaike information criterion as a performance metric. After analyzing four different probability distributions, the non-stationary model with annual rainfall as a covariate was found to be the best among all those examined, and the three-parameter lognormal the most frequently preferred distribution.

Depth-Duration-Frequency (DDF) curves are an essential tool in hydrological planning and risk management. However, the assumption of stationarity that is traditionally embedded in their derivation, is increasingly questioned by the impacts of climate change. This study focuses on adapting and projecting DDF curves for Sicily (Italy), which is experiencing an intensification of rainfall extremes, particularly for shorter durations. The proposed framework adapts the most up-to-date regional frequency analysis for the island by using an adaptation factor that incorporates the thermodynamic relationship between extreme precipitation and temperature, as well as future climate projections for temperature from an ensemble of regional climate models under the worst-case scenario. By the end of the century, the design rainfall estimates may require to be increased up to 50%, especially for hourly durations, to account for climate change effects. The results also highlight a strong spatial variability in the precipitation quantiles, with higher values observed in specific areas such as the north-eastern part of the island, which is characterized by small catchments and particularly prone to flash floods. Finally, this study provides a simple but still physical-based approach to updating DDF curves, that can be useful for engineers and practitioners, enhancing international efforts to mitigate climate change impacts through improved hydrological planning.

There is high confidence that precipitation extremes are projected to become more frequent and severe and, to a lesser extent, that their seasonality may change. However, these precipitation characteristics are dealt with separately, without examining whether magnitude and seasonality are jointly projected to change. Here we assess how the seasonality and magnitude of precipitation extremes are jointly projected to change for different climate scenarios. We perform analyses at the global scale using nine global climate models and four different emission scenarios. We identify large areas of the globe where the magnitude of the extremes is expected to increase as the emissions increase; at the same time, large changes in the seasonality of these extremes are projected to impact regions mainly located in the tropical and sub-tropical areas. These changes could impact our response and mitigation efforts and our resilience against such phenomena in response to climate change.

Extreme rainfall events have been more frequent in recent decades, potentially as a climate change effect. This has been leading to a higher risk of the failure of existing hydraulic infrastructures, and to a higher awareness regarding the unreliability of design rainfall calculated with reference to historical data recorded in the last century. With this in mind, the present study questions the stationary assumption of the rainfall Depth–Duration–Frequency curves commonly used in Sicily, the biggest island of the Mediterranean Sea. Quantiles derived from the most up-to-date regional method, regarding Sicily, based on observations in the period 1928–2010, have been compared with those extracted from a high-resolution dataset related to the period 2002–2022, provided by the SIAS agency. The results showed a remarkable underestimation of the rainfall quantiles calculated with the regional approach, especially at the shortest durations and low return periods. This means that new hydraulic works should be designed with reference to longer return periods than in the recent past, and those that currently exist may experience a higher risk of failure. Future investigation of this aspect is crucial for enhancing the effectiveness of water management and detecting hydrological risks under a changing climate.