Abstract
Comprehensive and robust statistical estimates of trends in heavy precipitation events are essential for understanding the impact of past and future climate change on the hydrological cycle. However, methods commonly used in extreme value statistics (EVS) are often unable to detect significant trends, because of their methodologically motivated reduction of the sample size and strong assumptions regarding the underlying distribution. Here, we propose linear quantile regression (QR) as a complementary and robust alternative to estimating trends in heavy precipitation events. QR does not require any assumptions on the underlying distribution and is also able to estimate trends for the full span of the distribution without any reduction of the available data. As an example, we study here a very dense and homogenized data set of daily precipitation amounts over Germany for the period 1951--2006 to compare the results of QR and the so-called block maxima approach, a classical method in EVS. Both methods indicate an overall increase in the intensity of heavy precipitation events. The strongest trends can be found in regions with an elevation of about 500~m above sea level. In turn, larger spatial clusters of moderate or even decreasing trends can only be found in Northeastern Germany. In conclusion, both methods show comparable results. QR, however, allows for a more flexible and comprehensive study of precipitation events.
Highlights
During the past decades, climate change has been one of the most intensively discussed topics in atmospheric science
We proposed quantile regression (QR) as a complementary and powerful statistical tool for extreme value statistics (EVS)
While QR estimates quantile curves from data in a fashion similar to ordinary least square (OLS), the block maxima (BM) approach aims to approximate the generalized extreme value (GEV) for a set of given BM. Both methods follow a different philosophy, a high correlation between the location parameter of the GEV and the 95th percentile estimated by QR could be found
Summary
Climate change has been one of the most intensively discussed topics in atmospheric science. The drivers behind the debates include the question of whether or why climate change is occurring, and the assessment of the potential impacts of climate change on nature and society. This applies in particular to changes in the frequency and intensity of meteorological extreme events such as droughts, heat waves, and heavy precipitation events, as these directly endanger people’s health and can cause tremendous damage to nature and infrastructure. Assessing the risks of future climate extremes requires a reliable and robust statistical framework. The results could lead to false decision making in risk management
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