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Abstract
A generalized time series decomposition technique is applied to monthly total precipitation data from a German station network of 132 time series covering 1901-2000. The decomposition technique shows that observed time series can be interpreted as a realization of a Gumbel distributed random variable with time-dependent location parameter and time-dependent scale parameter. It provides a full analytical description of the series in terms of the probability density function (PDF) for every time step of the observation period. Consequently, probability assessments of extreme values are possible for any threshold at any time. Most of the year, an increase in the probability of exceeding the 95th percentile and a decrease in the probability of falling under the 5th percentile can be detected at several stations in the southern part of Germany. In the western part, we observe the same phenomenon in the summer months, but these changes go along with smaller magnitudes. However, climate is getting more extreme in that region in winter: Probability for both exceeding the 95th percentile and for falling under the 5th percentile is increasing. In the eastern part of Germany, increases in the probability of occurrence of relatively low precipitation in winter as well as decreases in both probabilities (>95th percentile, <5th percentile) in summer and autumn prevail. [PUBLICATION ABSTRACT]