Pattern scaling can be used to linearly relate changes in extreme indices to changes in the annual or seasonal mean temperature. This study demonstrates the skills and limitations of two often used pattern scaling approaches in filling-in gaps in the time series of six temperature-related extreme indices. The extreme indices over Europe are derived from daily temperature output of 12 regional climate models of the multi-model project ENSEMBLES. The response pattern is estimated using one of the two future time periods (2021–2050 or 2070–2099) and the reference period (1961–1990). The simulated values from the remaining future time period are used for evaluating the skills. Both pattern scaling approaches perform reasonably well particularly for percentile-based and over most of the regions also for fixed temperature indices. Uncertainties due to internal variability can be large if the time period used for estimating the response pattern is close to the reference period. Limitations of pattern scaling due to violations of the linearity assumption are related to the shape of the temperature distribution. As a result, differences in the skills among the extreme indices can be related to the magnitude and shift direction of the whole temperature distribution. Therefore, skills for estimated extreme indices derived from the upper tail of the underlying temperature distribution are generally high. Over some areas, linear regression models used in this study are not appropriate statistical models because of the bounded and discrete nature of the data. Alternative pattern scaling methods such as, for instance, the logistic regression model leads to improvements over particular areas but not over the whole integration area. Copyright © 2013 Royal Meteorological Society
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