Annual Maximum Precipitation Research Articles

Urban signatures of sub-daily extreme precipitation events over a metropolitan region

Previous research on the relationship between urbanization and extreme precipitation has shown that regional and global anthropogenic changes have made extreme precipitation events more frequent. The purpose of this research was to explore changes in the variability of extreme precipitation in urban areas that underwent different degrees of urbanization intensity, and to explore the possible reasons using data from 7 meteorological stations in Shenzhen, China with hourly precipitation records dating back to the 60's. Shenzhen is one of the fastest growing metropolitan areas on earth with heavy urbanization occurring since 1979. We used k-means clustering to categorize areas of the city into steadily urbanized regions (SUR) or rapidly urbanized regions (RUR). Next, the variations in the linear trends of annual maximum precipitation (AMP) were analyzed, followed by water vapor analysis and the random forest algorithm to analyze the causes of the variation. The results showed that the severity of the urbanization process had a large and significant impact on extreme precipitation. The AMP analysis showed that at the higher temporal resolutions (< 6 h) extreme precipitation was only significant at RUR stations, while at low temporal resolutions precipitation did not significantly change at any station. An analysis of the diurnal patterns of precipitation and the water vapor analysis over Shenzhen showed that the increase in short-duration extreme precipitation events may be related to atmospheric instability caused by heat emitted as a byproduct of the urbanization process. Furthermore, the differences between the variations in the precipitation trends of SUR and RUR may be related to differences in thermal contrast between high and low altitudes. The results of the random forest analysis supported the correlations between the severity of urban development and changes in precipitation variability. Our findings have highlighted the need for a better understanding of the local and regional anthropogenic effects on the climate and the intensification of extreme climate events due to urbanization.

A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation

A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6.

Estimating Rainfall Design Values for the City of Oslo, Norway—Comparison of Methods and Quantification of Uncertainty

Due to its location, its old sewage system, and the channelling of rivers, Oslo is highly exposed to urban flooding. Thus, it is crucial to provide relevant and reliable information on extreme precipitation in the planning and design of infrastructure. Intensity-Duration-Frequency (IDF) curves are a frequently used tool for that purpose. However, the computational method for IDF curves in Norway was established over 45 years ago, and has not been further developed since. In our study, we show that the current method of fitting a Gumbel distribution to the highest precipitation events is not able to reflect the return values for the long return periods. Instead, we introduce the fitting of a Generalised Extreme Value (GEV) distribution for annual maximum precipitation in two different ways, using (a) a modified Maximum Likelihood estimation and (b) Bayesian inference. The comparison of the two methods for 14 stations in and around Oslo reveals that the estimated median return values are very similar, but the Bayesian method provides upper credible interval boundaries that are considerably higher. Two different goodness-of-fit tests favour the Bayesian method; thus, we suggest using the Bayesian inference for estimating IDF curves for the Oslo area.

Changes in Precipitation Extremes across Vietnam and Its Relationships with Teleconnection Patterns of the Northern Hemisphere

Understanding changes in precipitation extremes is critical for designing mitigation measures for the potential implications of a warming climate. This study assessed changes in the magnitude and frequency of precipitation extremes over Vietnam using high-quality gridded daily precipitation observations from 1980 to 2010. The annual maxima precipitation was analyzed to detect historical changes in the magnitude of precipitation extremes, while the number of heavy precipitation events, defined using the peak-over-threshold approach, was used to assess changes in the frequency of precipitation extremes. We found a strong signal of changes in the frequency of heavy precipitation, with 28.3% of Vietnam’s landmass exhibiting significant increasing trends. The magnitude of annual maxima precipitation shows a mixed pattern of changes, with less than 10% of Vietnam’s landmass exhibiting significant (both increasing and decreasing) trends. To identify possible mechanisms driving changes in precipitation, we assessed the relationship between inter-annual variations in precipitation extremes and climate variability represented by the teleconnection patterns of the Northern Hemisphere. Using five climate indices, we found that teleconnections across the Indian and Pacific Oceans have implied large control over the characteristics of precipitation extremes across Vietnam, with up to 30% of Vietnam’s landmass exhibiting a significant relationship.

Characterizing extreme rainfalls and constructing confidence intervals for IDF curves using Scaling‐GEV distribution model

AbstractThis article compares the performances of three fitting methods (SLmom, S1NCM, and S3NCM) to account for temporal characteristics of Annual Maximum Precipitations (AMPs) on daily and sub‐daily time scales using scaling General Extreme Value (GEV) distribution at a local site. Based on simple scaling properties of AMPs, the temporal downscaling model (called Scaling‐GEV) with parameter estimation methods are used to estimate sub‐daily AMPs from observed daily data. The feasibility and accuracy of the suggested method were assessed using rainfall data available from Dorval in Quebec (Canada) and Seoul (South Korea) for the period 1961–1990. Presence of simple scaling properties of AMPs for two stations has shown that it is feasible to use the temporal downscaling method for describing the linkage between AMPs of different time scales. Numerical and graphical analyses revealed that the Scaling‐GEV distribution by the Three‐Non central moments (NCM) method (S3NCM) provides the most accurate estimates compared to observed data amongst three fitting methods. In addition, this study suggested a modified bootstrap technique to determine confidence intervals (CIs) CIs of extreme rainfall series using the simple scaling properties of extreme rainfalls and only daily AMPs. Although the CIs were constructed by only daily AMPs and the simple scaling properties, the observed sub‐daily AMPs are generally within the 95% CI estimated.

Statistical modeling of annual maximum precipitation in Oued El Gourzi Watershed, Algeria

This study aims to model annual maximum precipitation based on extreme value theory for the Oued El Gourzi Watershed, Algeria. A generalized extreme value (GEV) distribution was used to determine the probability distribution of extreme values and their dependency on time for the five stations distributed across the watershed. The non-stationary models are used to represent the GEV parameters assumed an invariant shape parameter and linear functions as location and scale parameters. The best model was selected using Akaike’s information criterion and Bayesian information criterion. Stationary and non-stationary return levels for different return periods have been proposed for the study area.

Adaptive fitness of Sapindus emarginatus Vahl populations towards future climatic regimes and the limiting factors of its distribution

Sapindus emarginatus Vahl (Sapindaceae) also known as ‘Indian Soap nut’ is significantly important for saponin content in its fruits. However, its current population in India is heavily fragmented due to a lack of sustainable harvesting practices. Moreover, changing climatic regimes may further limit its distribution and possibly compromise the survival of the species in nature. The aim of the present study was to: predict the future distribution range of S. emarginatus; identify the bioclimatic variables limiting this distribution and to evaluate its adaptive fitness and genomic resilience towards these variables. To determine future species distribution range and identify limiting bioclimatic variables, we applied two different ecological niche models (ENMs; BioClim and MaxEnt) on real occurrence data (n = 88 locations). The adaptive fitness of the species was evaluated by quantifying the genetic variability with AFLP markers and marker-environmental associations, using AFLP-associated Bayesian statistics. We found 77% overlap between the baseline (2030) and predicted (2100) species distribution ranges, which were primarily determined by maximum temperature (TMAX) and mean annual precipitation (MAP). The TMAX and MAP contributed 43.1% and 27.1%, respectively to ENM model prediction. Furthermore, AFLP loci significantly associated with bioclimatic variables, and TMAX and MAP represent the lowest proportion (6.15%), confirming to the severe response of the species genome towards these variables. Nevertheless, the very low Linkage disequilibrium (LD) in these loci (4.54%) suggests that the current sensitivity to TMAX and MAP is subject to change during recombination. Moreover, a combination of high heterozygosity (0.40–0.43) and high within-population variability (91.63 ± 0.31%) confirmed high adaptive fitness to maintain reproductive success. Therefore, the current populations of S. emarginatus have substantial genomic resilience towards future climate change, albeit significant conservation efforts (including mass multiplication) are warranted to avoid future deleterious impacts of inbreeding depression on the fragmented populations.

Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul

In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena.

ROUTINE, EXTREME AND ENGINEERING METEOROLOGY ANALYSIS FOR KARACHI COASTAL AREA

Pakistan’s largest city, Karachi, which is industrial centre and economic hub needs focus in research and development of every field of Engineering, Science and Technology. Urbanization and industrialization is resulting bad weather conditions which prolongs until a climate change. Since, Meteorology serves as interdisciplinary field of study, an analytical study of real and region-specific meteorological data is conducted which focuses on routine, extreme and engineering meteorology of metropolitan city Karachi. Results of study endorse the meteorological parameters relationship and establish the variability of those parameters for Karachi Coastal Area. The rise of temperature, decreasing trend of atmospheric pressure, increment in precipitation and fall in relative humidity depict the effects of urbanization and industrialization. The recorded extreme maximum temperature of 45.50C (on June 11, 1988) and the extreme minimum temperature of 4.5 0C(on January 1, 2007) is observed at Karachi south meteorological station. The estimated temperature rise in 32 years is 0.9 0C, which is crossing the Intergovernmental Panel on Climate Change (IPCC) predicted/estimated limit of 2oC rise per century. The maximum annual precipitation of 487.0mm appearing in 1994 and the minimum annual precipitation of 2.5mm appearing in 1987 is observed at same station which is representative meteorological station for Karachi Coast. Further Engineering meteorological parameters for heating ventilation air condition (HVAC) system design for industrial purpose are deduced as supporting data for coastal area site study for industrial as well as any follow-up engineering work in the specified region.

Trend of extreme rainfall events using suitable Global Circulation Model to combat the water logging condition in Kolkata Metropolitan Area

Kolkata Metropolitan Area (KMA) is one of the most densely populated areas in the world, where extreme precipitation events result in frequent waterlogging and flood inundation conditions which can adversely affect the city. In this study, we evaluate the nature and trend of annual-maximum precipitation events by employing suitable Global Circulation Models (GCMs). The trend of observed annual maximum daily rainfall from 1901 to 2013 has been analysed through linear regression, recurrence period and multi-decadal oscillation. The results show that the observed rainfall of the given region follows the natural variability and oscillation trend. TRMM time-series data (2000–2019) is employed to validate the ground-station data series using Modified Mann Kendall and Sen’s Slope estimator test, which shows a correlation of 0.88. In addition, suitable bias-corrected GCMs have been selected to estimate the future precipitation in the given study area. Analysis of future rainfall for the period of 2071–2100 revealed that the RCPs are showing an increase in the extreme precipitation intensity by 1.5, 2.3 and 3 times considering RCP 4.5, 6.0 and 8.5 scenarios respectively. The presented results serve as the preliminary data for future planning and designing efficient flood management strategies in the given area to combat water-logging in KMA.

Analysis of Annual and Seasonal Precipitation Variation in the Qinba Mountain area, China

In this study, the spatial and temporal characteristics in precipitation extremes, total precipitation, and the seasonality of precipitation of the Qinba Mountain in China were investigated from 1961 to 2015, based on daily precipitation data of 37 meteorological stations. The results from our study suggest that: the annual precipitation in the area varies between 645 mm and 2175.2 mm, with the minimum annual precipitation occurred in 1997, and the maximum annual precipitation, in 1963. Between 1961 and 2015, a significant decreasing trend was observed in the annual precipitation, suggesting a decrease of −21.1 mm/a. The spatial distribution of precipitation in the Qinba Mountain area increased from the north to the south, showing an obvious difference in precipitation between the two areas. For extreme indices, the trends of annual total wet-day precipitation (PRCPTOT), number of heavy precipitation days (R10mm)and consecutive wet days (CWD)showed a downward trend, while the other extreme indices had an upward trend. The results from our research not only help the researchers to understand the characteristics of precipitation, but also provide crucial information for the policy makers to make better decisions, in future.

Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

AbstractProjections of changes in extreme climate are sometimes predicted by multimodel ensemble methods that combine forecasts from individual simulation models using weighted averaging. One method to assign weight to each model is the Bayesian model averaging (BMA) in which posterior probability is used. For the cases of extreme climate, the generalized extreme value distribution (GEVD) is typically used. We applied the approach of GEV‐embedded BMA to a series of 35 years of the annual maximum daily precipitation data (both historical data and data gathered from simulation experiments for future periods) over the Korean peninsula as simulated by the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulation data under two Representative Concentration Pathway (RCP) scenarios, namely RCP4.5 and RCP8.5, were used. Observed data and 17 CMIP5 models for 12 gird cells in Korea have been examined to predict future changes in precipitation extremes. A simple regional frequency analysis of pooling observations from three stations in each cell was employed to reduce the estimation variance and local fluctuations. A bias correction technique using the regression‐type transfer function was applied to these simulation data. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1971–2005), were estimated for two future periods, namely Period 1 (2021–2050) and Period 2 (2066–2095). From these analyses, relative increase observed in the spatially averaged 20‐year (50‐year) return level was approximately 23% (16%) and 45% (36%) in the RCP4.5 and RCP8.5 experiments, respectively, by the end of the 21st century. We concluded that extreme rainfalls will likely occur two times and four times more frequently in the RCP4.5 and RCP8.5 scenarios, respectively, as compared to in the reference years by the end of the 21st century.

Global and Regional Projected Changes in 100-yr Subdaily, Daily, and Multiday Precipitation Extremes Estimated from Three Large Ensembles of Climate Simulations

AbstractMany studies have reported projected increases in the frequency and intensity of extreme precipitation events in a warmer future climate. These results challenge the assumption of climate stationarity, a standard hypothesis in the estimation of extreme precipitation quantiles (e.g., 100-yr return period) often used as key design criteria for many infrastructures. In this work, changes in hourly to 5-day precipitation extremes occurring between the 1980–99 and 2080–99 periods are investigated using three large ensembles (LE) of climate simulations. The first two are the global CanESM2 50-member ensemble at a 2.8° resolution and the global CESM1 40-member ensemble at a 1° resolution. The third is the regional CRCM5 50-member ensemble at a 0.11° resolution, driven at its boundaries by the 50-member CanESM2 ensemble over the northeastern North America (NNA) and Europe (EU) domains. Results indicate increases in the frequency of future extreme events, and, accordingly, a reduction of the return period of current extreme events for all tested spatial resolutions and temporal scales. Agreement between the three ensembles suggests that extreme precipitations, corresponding to the 100-yr return period over the reference period, become 4–5 (2–4) times more frequent on average for the NNA (EU) domain for daily and 5-day annual maximum precipitation. Projections by CRCM5-LE show even larger increases for subdaily precipitation extremes. Considering the life-span of many public infrastructures, these changes may have important implications on service levels and the design of many water infrastructures and for public safety, and should therefore be taken into consideration in establishing design criteria.

Assessing the Regional Concept with Sub-Sampling Approach to Identify Probability Distribution for at-Site Hydrological Frequency Analysis

The framework of regional analysis allows superior discrimination as well as better identification of the shape of a population distribution in a hydrological frequency analysis. The aim of this study is to incorporate the better of regional concept while performing an at-site frequency analysis. The study proposes a new method in the form of sub-sampling technique with the aid of a regional distribution selection procedure to choose an appropriate probability distribution function for frequency analysis. The technique is evaluated against common distribution selection methods: a widely used goodness-of-fit method in Anderson–Darling (AD) and a popular graphical assessment tool in L-moment ratio diagram (LMRD). The performance is evaluated by applying the technique to gauged annual maximum daily precipitation data series of 24 stations located across China. It is found that the technique accomplished a better performance in discriminating among distributions which or else may not be achievable only by the AD or LMRD test. In general, all results indicate that the proposed technique can be an attractive means in discriminating as well as identifying the best distribution for at-site frequency analysis.

Nonstationary influence of the North Atlantic tropical cyclones on the spatio‐temporal variability of the eastern United States precipitation extremes

AbstractIn the eastern United States, the empirical probability distribution of extreme daily precipitation comprises heavy rainfall events stemming from North Atlantic tropical cyclones (TCs). At many locations, these events influence estimates of extreme value statistics (e.g., 100‐year event), thus have important bearing on the sizing of flood protection infrastructure and, in general, flood risk management and preparedness. Consequently, a characterization of location specific and regional patterns in precipitation extremes and changes therein has salience for both scientific and engineering concerns. To this end, analysis of seasonal and annual maximum daily precipitation at 667 long‐term stations across the eastern United States was pursued to ascertain recent changes in the extreme events over the 1950–2011 period. Three key results from this study illuminate less understood facets of recent changes in precipitation extremes: (a) an overall increase in the fraction of seasonal and annual maximum precipitation events linked to TCs, (b) a dramatic increase in the correlation between Accumulated Cyclone Energy Index and the leading principal component of extreme precipitation, and (c) changes in the spatial patterns of regions with highest TC‐related risk for heavy precipitation.

Assessment of Changes in Annual Maximum Precipitations in the Iberian Peninsula under Climate Change

Climate model projections can be used to assess the future expected behavior of extreme precipitation due to climate change. In Europe, the EURO-CORDEX project provides precipitation projections in the future under various representative concentration pathways (RCP), through regionalized outputs of Global Climate Models (GCM) by a set of Regional Climate Models (RCM). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX project are analyzed in the Iberian Peninsula and the Balearic Islands. Precipitation quantiles for a set of exceedance probabilities are estimated by using the Generalized Extreme Value (GEV) distribution function fitted by the L-moment method. Precipitation quantiles expected in the future period are compared with the precipitation quantiles in the control period, for each climate model. An approach based on Monte Carlo simulations is developed to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period to identify statistically significant changes. The higher the significance threshold, the fewer cells with changes are identified. Consequently, a set of maps are obtained for various thresholds to assist the decision making process in subsequent climate change studies.

Advances in Deriving the Exact Distribution of Maximum Annual Daily Precipitation

Maximum annual daily precipitation does not attain asymptotic conditions. Consequently, the results of classical extreme value theory do not apply to this variable. This issue has raised concerns about the frequent use of asymptotic distributions to model the maximum annual daily precipitation and, at the same time, has rekindled interest in deriving and testing its exact (or non-asymptotic) distribution. In this review, we summarize and discuss results to date about the derivation of the exact distribution of maximum annual daily precipitation, with attention on compound/superstatistical distributions.

Role of Extreme Precipitation and Initial Hydrologic Conditions on Floods in Godavari River Basin, India

AbstractFloods are the most frequent natural calamity in India. The Godavari river basin (GRB) witnessed several floods in the past 50 years. Notwithstanding the large damage and economic loss, the role of extreme precipitation and antecedent moisture conditions on floods in the GRB remains unexplored. Using the observations and the well‐calibrated Variable Infiltration Capacity model, we estimate the changes in the extreme precipitation and floods in the observed (1955–2016) and projected future (2071–2100) climate in the GRB. We evaluate the role of initial hydrologic conditions and extreme precipitation on floods in both observed and projected future climate. We find a statistically significant increase in annual maximum precipitation for the catchments upstream of four gage stations during the 1955–2016 period. However, the rise in annual maximum streamflow at all the four gage stations in GRB was not statistically significant. The probability of floods driven by extreme precipitation (PFEP) varies between 0.55 and 0.7 at the four gage stations of the GRB, which declines with the size of the basins. More than 80% of extreme precipitation events that cause floods occur on wet antecedent moisture conditions at all the four locations in the GRB. The frequency of extreme precipitation events is projected to rise by two folds or more (under RCP 8.5) in the future (2071–2100) at all four locations. However, the increased frequency of floods under the future climate will largely be driven by the substantial rise in the extreme precipitation events rather than wet antecedent moisture conditions.

Development of a Pressure–Precipitation Transmitter

AbstractA novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.

Projected Changes in the Probability Distributions, Seasonality, and Spatiotemporal Scaling of Daily and Subdaily Extreme Precipitation Simulated by a 50‐Member Ensemble Over Northeastern North America

AbstractGlobal warming is expected to produce modifications in the intensity, as well as in the seasonality and spatiotemporal structure of extreme precipitation. In the present study, the temporal evolution of simulated daily and subdaily precipitation extremes was analyzed to assess how they respond to climate warming over different time horizons. Pooling series from the recent 50‐member Canadian Regional Climate Model v5 Large Ensemble, the probability distributions, date and time of occurrences, and spatiotemporal structure of simulated Annual Maxima (AM) precipitation were analyzed at various spatial scales and for durations between 1 hr and 3 days. In agreement with previous studies, the results underline the large increases in AM precipitation quantiles, especially for the shortest durations and for the more extreme events (i.e., longest return periods), and modifications in their spatiotemporal scaling properties and annual and diurnal cycles. For instance, subdaily AM extremes are expected to occur later in the evening, while, no matter the duration, the extremes are expected to occur over a wider period of the year in future climate. Finally, the analysis of projected AM probability distributions showed that heavy‐tail Generalized Extreme Value (GEV) distributions will most likely be observed in the future climate, with some model grid boxes experiencing a significant increase of GEV shape parameters. These results may have major consequences in terms of the occurrence and possible impacts of the most extreme precipitation events.