Extreme precipitation trends and events are fundamental for the definition of the region’s climate and allow the subsequent analysis of the risk for the territory and the possible countermeasures. This study takes into account the Marche Region (Central Italy) with 128 rain gauges from 1921 to 2017. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. In addition, to obtain a comparable analysis, the average precipitations grouped in climatological standard normals were analyzed. Finally, the main purpose of the research was achieved by analyzing extreme events in the middle Adriatic side. In addition, forecasts of extreme precipitation events, with a return period of 100 years, were made using the theory of “generalized extreme value” (GEV). The innovation of this research is represented by the use of geostatistics to spatialize the variables investigated, through a clear and immediate graphic representation performed through GIS software. This study is a necessary starting point for the study of climate dynamics in the region, and it is also a useful tool for land use planning.

Potential evapotranspiration (ET0) is an indicator of great interest for water budget analysis and the agricultural sector. Therefore, the purpose of this study was to make the calculation reliable even if only the temperature data were present. In this research, the ET0 was initially calculated for a limited number of weather stations (12) using the Penman–Monteith method. In some cases, the simplified Penman–Monteith formula was adopted, while in others, as in the case of mountain weather stations, the complete formula was employed to consider the differences in vegetation, deduced from satellite surveys. Subsequently, the ET0 was calculated with the Hargreaves–Samani (HS) formula, calibrating the Hargreaves coefficient, through the spatialization of ET0, by the geostatistical method. The results showed a high reliability of the HS method in comparison with simplified PM (PM) method, and complete Penman–Monteith (cPM) method, with a minimum calibration of the empirical Hargreaves coefficient. In particular, a very good correlation between the results obtained in the mountain environment with the uncalibrated HS method and the cPM method was also observed in this area, while PM showed discordant and much higher results than ET0 compared with the other methods. It follows that this procedure allowed a more accurate estimate of potential evapotranspiration with a view to territory management, both in terms of water resources and the irrigation needs of the vegetation.

This study provides a unique procedure for validating and reconstructing temperature and precipitation data. Although developed from data in Middle Italy, the validation method is intended to be universal, subject to appropriate calibration according to the climate zones analysed. This research is an attempt to create shared applicative procedures that are most of the time only theorized or included in some software without a clear definition of the methods. The purpose is to detect most types of errors according to the procedures for data validation prescribed by the World Meteorological Organization, defining practical operations for each of the five types of data controls: gross error checking, internal consistency check, tolerance test, temporal consistency, and spatial consistency. Temperature and precipitation data over the period 1931–2014 were investigated. The outcomes of this process have led to the removal of 375 records (0.02%) of temperature data from 40 weather stations and 1286 records (1.67%) of precipitation data from 118 weather stations, and 171 data points reconstructed. In conclusion, this work contributes to the development of standardized methodologies to validate climate data and provides an innovative procedure to reconstruct missing data in the absence of reliable reference time series.

The extreme rainfall events of recent years in central Italy are producing an increase in hydrogeological risk, with disastrous flooding in terms of human lives and economic losses, as well as triggering landslide phenomena in correspondence with these events. A correct prediction of 100-year return levels could encourage better land planning, sizing works correctly according to the expected extreme events and managing emergencies more consciously through real-time alerts. In the recent period, it has been observed that the return levels predicted by the main forecasting methods for extreme rainfall events have turned out to be lower than observed within a few years. In this context, a model widely used in the literature, the generalised extreme value (GEV) with the “block maxima” approach, was used to assess the dependence of this model on the length of the collected precipitation time series and the possible addition of years with extreme events of great intensity. A total of 131 rainfall time series were collected from the Adriatic slope in central Italy comparing two periods: one characterised by 70 years of observations (1951–2020), the other by only 30 years (1991–2020). At the same time, a decision was made to analyse what the effect might be—in terms of the 100-year return level—of introducing an additional extreme event to the 1991–2020 historical series, in this case an event that actually occurred in the area on 15 September 2022. The results obtained were rather surprising, with a clear indication that the values of the 100-year return level calculated by GEV vary according to the length of the historical series examined. In particular, the shorter time series 1991–2020 provided higher return level values than those obtained from the 1951–2020 period; furthermore, the addition of the extreme event of 2022 generated even higher return level values. It follows that, as shown by the extreme precipitation events that have occurred in recent years, it is more appropriate to consider a rather short period because the ongoing climate change does not allow true estimates to be obtained using longer time series, which are preferred in the scientific literature, or possibly questioning the real reliability of the GEV model.