Several studies showed the impact of global climate change in Germany and Saxony including the risk of increasing precipitation extremes. Here, heavy precipitation was analyzed on the basis of daily precipitation sums using the 95th percentile (index R95p). The long term development was studied for selected stations (1917–2013). Transects with high spatial resolution (1×1 km) (1961–2015) complemented the study to gain information about spatial temporal development of the occurrence of precipitation extremes. The non parametric kernel occurrence rate estimation has been applied to reveal changes in the temporal development of daily totals. The most distinct changes have been found for the seasons and the growing seasons and only slight changes for the calendar year and the meteorological half-years. The findings of this study showed a shifting seasonality with decreasing number of heavy precipitation events in the growing season I (April, May, June) and increasing number of events in growing season II (July, August, September). Furthermore, a distinct periodicity has been revealed in all findings for all seasons, particularly striking in the growing seasons, indicating the influence of large scale drivers as potentially the North Atlantic Oscillation on local precipitation extremes. Our data showed, that kernel occurrence rate estimation is a suitable approach to analyze the temporal development of heavy precipitation with a high temporal and spatial resolution.

中文翻译：

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气候动态：德国萨克森州强降水发生频率的时间变化

多项研究表明，全球气候变化对德国和萨克森州的影响包括增加极端降水的风险。在这里，使用第95个百分位数（指数R95p）根据每日降水量对强降水进行了分析。对选定电台的长期发展进行了研究（1917年至2013年）。具有高空间分辨率（1×1 km）（1961–2015）的样条补充了该研究，以获取有关降水极端事件发生的时空发展信息。非参数内核发生率估计已应用于揭示日总数的时间发展变化。在季节和生长季节中发现了最明显的变化，而日历年和气象半年则只有很小的变化。这项研究的结果表明，季节性变化，第一生长季节（4月，5月，6月）的强降水事件数量减少，第二生长季节（7月，8月，9月）的强降水事件数量增加。此外，在所有季节的所有发现中都揭示出明显的周期性，特别是在生长季节中的显着时期，表明大规模驱动因素（可能是北大西洋涛动）对当地极端降水的影响。我们的数据表明，核仁发生率估计是一种以高时空分辨率分析大降水的时间变化的合适方法。此外，在所有季节的所有发现中都揭示出明显的周期性，特别是在生长季节中的显着时期，表明大规模驱动因素（可能是北大西洋涛动）对当地极端降水的影响。我们的数据表明，核仁发生率估计是一种以高时空分辨率分析大降水的时间变化的合适方法。此外，在所有季节的所有发现中都揭示出明显的周期性，特别是在生长季节中的显着时期，表明大规模驱动因素（可能是北大西洋涛动）对当地极端降水的影响。我们的数据表明，核仁发生率估计是一种以高时空分辨率分析大降水的时间变化的合适方法。