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Observed trends, theory and modelling results all suggest increases in future extreme precipitation due to climate warming. The largest increases are expected in short‐duration events with less than a day. Relatively few previous studies have focused specifically on the projection of sub‐daily precipitation extremes. In this study, a statistical downscaling method based on circulation patterns (CPs) is developed to project site‐specific extreme hourly precipitation over the UK. First, a CP‐classification categorizes extreme hourly precipitation events based on the underlying atmospheric pressure conditions on each day. An analogue day method is then used to find for each future day the most similar day in the past by comparing the predictor values of daily precipitation and temperature simulated by Regional Climate Models (RCMs) with observations conditioned on different CPs and seasons. Finally, the maximum hourly precipitation records on the most similar days are extracted and perturbed using precipitation duration‐temperature relationships. The applied statistical downscaling method is a combination of the analogue and the regression‐based method. It is found that the statistical downscaling method is able to reproduce observed extreme hourly precipitation. In terms of future changes under a warmer climate, it is shown that increases in extreme hourly precipitation can be as high as 112% but are highly variable depending on the rainfall stations, the future time periods, the emission scenarios, and the different RCM runs.

For climate impact assessment regarding hydrology, the availability of long precipitation time series with high temporal and spatial resolution is essential. A possible approach to obtain this data is the statistical downscaling of precipitation simulated by a global climate model (GCM) using a stochastic rainfall model with parameters conditioned on circulation patterns (CP). This approach requires: (1) the existence of a strong relationship between CP and precipitation, (2) the sufficient reproduction of CPs by the GCM, (3) the adequate simulation of precipitation by the rainfall model and (4) either stationarity of the relationship between precipitation and CPs or an approach to account for non-stationarity. The objective of this research is the careful evaluation and discussion of the above stated four hypotheses. For this purpose, a case study for the Aller–Leine river basin in Northern Germany has been created. It has been found that CPs can be defined which show significant differences in precipitation behaviour. The CPs derived from re-analysis data are well reproduced by the GCM simulations. In addition, the hourly stochastic rainfall model simulates the observed precipitation characteristics well, except for a certain overestimation of the extremes. However, the change in rainfall between past and future time periods as predicted by a regional climate model could not be explained by the change in CP frequency, due to the non-stationarity of the relationship between rainfall and CP. This can be best accounted for by re-estimating the parameters of the stochastic rainfall model for future conditions based on corrected observations using a delta change approach regarding simulated rainfall from a regional climate model.

The combination of a conventional Markov chain model (zero and first order) and a gamma distribution model are found to be applicable to derive meaningful agricultural features from precipitation in the Volta Basin (West Africa). Since the analysis of the monthly or annual precipitation amount does not provide any adequate information on rainfall timing and sufficiency of crop water requirement, rainfall modelling was performed on a daily time scale for 29 rainfall stations. The modelled rainfall features follow distinct spatial patterns, which will be presented as maps of(1) rainfall occurrence probabilities and (2) recommendations of optimal planting dates. In addition, the effective drought index (EDI) working on daily time scales is calculated in order to assess drought properties of five different rainfall regions within the Volta Basin. Apart from the common way of separately modelling the duration and intensity due to their different distributions, a copula approach is chosen in this study to construct a bivariate drought distribution. Application of the measures derived to agricultural decision support will be discussed briefly. Copyright © 2009 Royal Meteorological Society

Two statistical downscaling methods have been tested in terms of their ability to construct indices of extremes of daily precipitation and temperatures from large-scale atmospheric variables with the aim of developing a tool for the construction of future scenarios of the extremes. One of the methods implements an approach for constructing seasonal indices of extremes of precipitation and temperature from seasonal measures of large-scale variables, while the other method implements a stochastic model for generating daily series of precipitation and temperature whose parameters are conditioned on large-scale circulation patterns. While both models generally tend to perform fairly well in reproducing indices of precipitation in winter, their performance for the summer season is not attractive. For indices of temperature, the performance of both models is better than the corresponding performance for indices of precipitation and the seasonal variation in performance is less prominent. The models were applied to construct scenarios of the extremes for the end of the 21st century using predictor sets simulated by the Hadley Centre GCM (HadAM3P) forced by two of the special report on emission scenarios (SRES) emission scenarios. Both models project an increase in both the mean daily minimum and mean daily maximum temperatures for future climate change scenarios in all seasons. The summer increase is accompanied by an increase in the inter-annual variability of the temperatures. On the other hand, they show consistency in the direction of the projected changes in indices of precipitation only in winter, where they projected an increase in both the magnitude and frequency of extremes as well as the mean precipitation. The disparity in the changes simulated by the two models revealed the existence of considerable inter-model uncertainty in predicting changes for future climate. Copyright © 2007 Royal Meteorological Society

The evolution of daily extreme precipitation and temperature from 1958 to 2001 was investigated within the German side of the Rhine basin. Trends of a set of extreme precipitation and temperature indices defined on daily time series of precipitation and temperature were calculated at 611 precipitation and 232 temperature stations located within the study area and their corresponding significances were tested using the non-parametric Kendall- tau test. The results obtained indicated that both the daily minimum and maximum extreme temperatures have increased over the investigation period, with the degree of change showing seasonal variability. On an annual basis, the change in the daily minimum extreme temperature was found to be greater than that of the daily maximum extreme temperature. The daily extreme heavy precipitation has shown increasing trends both in magnitude and frequency of occurrence in all seasons except summer, where it showed the opposite trend. The station values of the daily precipitation were also interpolated on a regular grid of 5 km × 5 km so that the changes in the indices could be investigated on areal precipitation by aggregating the interpolated precipitation to any desired scale. This enables assessment of the hydrological consequences of the changes in the extreme precipitation. Although the spatial pattern remained more or less similar with that of the point-scale trends for all indices, the average trend magnitude showed an increase with the scale of the area on which precipitation was aggregated. Copyright © 2005 Royal Meteorological Society

A fuzzy rule-based methodology is applied to the problem of classifying daily atmospheric circulation patterns (CP). The purpose of the approach is to produce a semi-automated classification that combines the expert knowledge of the meteorologist and the speed and objectivity of the computer. Rules are defined corresponding to the geographical location of pressure anomalies. A CP is described by the location of four different possible pressure anomalies. The rules are formulated with fuzzy sets, allowing a certain flexibility because slightly different pressure maps may correspond to a given CP. Accordingly the degree of fulfilment of a rule is defined in order to measure the extent to which a pressure map may indeed belong to a CP type. As an output of the analysis, the CP on any given day is assigned to one, and only one, CP type to a varying degree of credibility. The methodology is applied to a European case study. The subjective classification of European CPs given by Hess and Brezowsky provides a basis for constructing the rules. The classification obtained can be used, for example, to simulate local precipitation conditioned on the 700 hPa pressure field. The information content of the fuzzy classification as measured by precipitation-related indices is similar to that of existing subjective classifications. The fuzzy rule-based approach thus has potential to be applicable to the classification of GCM produced daily CPs for the purpose of predicting the effect of climate change on space-time precipitation over areas where only a rudimentary classification exists or where none at all exists.