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A recent study showed a rise in total and extreme precipitation in the Netherlands over the past century. The present study attempts to characterize and attribute the seasonal variation of daily extreme precipitation events in the Netherlands. Statistical models for extreme values were used to fit daily rainfall maxima for all months during the period 1961–2014, using data from the 231 rain gauges distributed across the country. A generalized extreme value (GEV) approach was used to determine the probability distribution of extreme values and their dependency on time and the monthly North Atlantic Oscillation (NAO) index. The non-stationary models used to represent the annual cycle of the GEV parameters assumed an invariant shape parameter and harmonic functions as location and scale parameters. The best non-stationary model was selected using Akaike’s information criterion (AIC) and the log-likelihood ratio test (LRT). The results indicated that the estimates derived from the non-stationary model differed from those obtained with the aid of the stationary model, and had lower uncertainties. These non-stationary estimates were within the confidence intervals (CI) of the stationary estimates at most rain gauge stations. The non-stationary model estimated parameters with less uncertainty and with smaller CI, thus permitting more accurate representation of extreme precipitation in the Netherlands. The spatial pattern of annual mean location and scale GEV parameters was compatible with coastal, land cover (such as the wooded and heathland areas of the Veluwe region of the province of Gelderland) and orography (in the southeast of the country). The location parameter peaked over the west coast, especially on the central west coast during the summer half-year, while the centre and east of the country had the highest values during the winter half-year. The scale parameter peaked in the centre of the country during the summer, in the east in the early summer and along the west coast in the spring. The 10-year and 50-year return levels were calculated with the aid of the non-stationary model for all months. The spatial distribution of these extreme event probability clearly reflects the regional differences in the Netherlands.

Abstract: The debate on the enhanced greenhouse effect continues, confusing the climate change impact analysis and the decision makers. Hence, this paper is addressed to the extreme regional-scale evaluation of global climate model (GCM). Given that assessment of models is crucial to consider future scenarios in climate change impact analyses, the exploratory work is essential, as it identifies the strengths and weaknesses of GCM to regional-scale differences in model performance, providing insights on regional-scale climate interactions and processes. That is why this manuscript attempts to compare the spatial pattern of a longer temporal aggregation period (1960/61–1989/90) for extreme temperatures simulated by a particular GCM (HadCM3) to that of NCEP Reanalyses, which are considered as “truth”. Climatic risk analyses and forecasting, concern among other topics, assess an interpretation in intensity and frequency of recurrence of long duration extreme values (heat/cold waves). One study assesses the impact of extreme events analysing the temporal dependence of extreme events. The present study leads to the conclusion that the HadCM3 Simulations do not reproduce reasonably the NCEP Reanalyses over the Modelling the Impact of Climate Extremes (MICE—EU Project) domain, even despite the fact that the climatology of extremes has demonstrated very similar spatial patterns. Consequently, all diagnostics of extremes led support to the fitted Generalised Extreme Value (GEV) model for extrapolation, suggesting that the HadCM3 is not adequate for forecasting, simulating future scenarios of extremes based on the analysis of risk or probability maps for spatial uncertainties. The greatest difference between the two models is in terms of the precision of estimation. [Copyright &y& Elsevier]