Modeling univariate block maxima by the generalized extreme value distribution constitutes one of the most widely applied approaches in extreme value statistics. It has recently been found that, for an underlying stationary time series, respective estimators may be improved by calculating block maxima in an overlapping way. A proof of concept is provided that the latter finding also holds in situations that involve certain piecewise stationarities. A weak convergence result for an empirical process of central interest is provided, and, as a case-in-point, further details are worked out explicitly for the probability weighted moment estimator. Irrespective of the serial dependence, the estimation variance is shown to be smaller for the new estimator, while the bias was found to be the same or vary comparably little in extensive simulation experiments. The results are illustrated by Monte Carlo simulation experiments and are applied to a common situation involving temperature extremes in a changing climate.

翻译：以普遍极端值分布来模拟单一等离子体区块最大值,是极端值统计中最广泛采用的方法之一,最近发现,对于一个基本固定时间序列,可以通过重叠计算区块最大值来改进各自的估计数字; 提供概念证明,后一项发现也存在于涉及某些片段固定性的情况中; 为具有核心利益的实证过程提供了微弱的趋同结果,作为一例,为概率加权时点估计数字者明确制定了进一步的细节; 无论序列依赖性如何,新的估计数字的估计数差异都显示较小,而发现在广泛的模拟实验中,偏差相同或变化不大,结果通过蒙特卡洛模拟实验加以说明,并应用于变化气候中温度极端的常见情况。