Abstract. During a 15-day episode from 26 May to 9 June 2016, Germany was affected by an exceptionally large number of severe thunderstorms. Heavy rainfall, related flash floods and creek flooding, hail, and tornadoes caused substantial losses running into billions of euros (EUR). This paper analyzes the key features of the severe thunderstorm episode using extreme value statistics, an aggregated precipitation severity index, and two different objective weathertype classification schemes. It is shown that the thunderstorm episode was caused by the interaction of high moisture content, low thermal stability, weak wind speed, and large-scale lifting by surface lows, persisting over almost 2 weeks due to atmospheric blocking.For the long-term assessment of the recent thunderstorm episode, we draw comparisons to a 55-year period regarding clusters of convective days with variable length (2-15 days) based on precipitation severity, convection-favoring weather patterns, and compound events with low stability and weak flow. It is found that clusters with more than 8 consecutive convective days are very rare. For example, a 10-day cluster with convective weather patterns prevailing during the recent thunderstorm episode has a probability of less than 1 %. IntroductionBetween the end of May and mid-June 2016, Germany and large parts of central and southern Europe were affected by an exceptionally large number of severe convective storms and related extremes such as heavy rainfall, hail, and tornadoes (Fig. 1). Rain totals exceeding 100 mm within a few hours at several locations in Germany triggered various flash floods and floods mainly in small catchments. In the town of Braunsbach in the federal state of Baden-Württemberg, for example, a severe flash flood on 29 May with a height of up to 3.5 m caused serious damage to more than 80 buildings, of which five were completely lost (Daniell et al., 2016). Only 3 days later on 1 June, extreme rain in the district of Rottal-Inn in the south of Bavaria evoked a sudden and dramatic rise in the levels of several creeks such as the Simbach, where the height increased from 20 cm to more than 5 m within only 12 h. Subsequently, the village Simbach am Inn experienced the largest flooding in history. Some of the thunderstorms during the 2 weeks also produced hail with diameters between 0.5 and 5 cm. A total of 12 tornadoes in 8 days with intensities between F0 and F1 on the Fujita intensity scale, were recorded and confirmed by the European Severe Weather Database (ESWD; Dotzek et al., 2009).The severe thunderstorms caused substantial damage to buildings, infrastructures, transportation networks, and crops. A large number of roads and railroads were blocked or severely damaged, and some villages experienced power outPublished by Copernicus Publications on behalf of the European Geosciences Union.

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Abstract. Since 1990, natural hazards have led to over 1.6 million fatalities globally, and economic losses are estimated at an average of around USD 260–310 billion per year. The scientific and policy communities recognise the need to reduce these risks. As a result, the last decade has seen a rapid development of global models for assessing risk from natural hazards at the global scale. In this paper, we review the scientific literature on natural hazard risk assessments at the global scale, and we specifically examine whether and how they have examined future projections of hazard, exposure, and/or vulnerability. In doing so, we examine similarities and differences between the approaches taken across the different hazards, and we identify potential ways in which different hazard communities can learn from each other. For example, there are a number of global risk studies focusing on hydrological, climatological, and meteorological hazards that have included future projections and disaster risk reduction measures (in the case of floods), whereas fewer exist in the peer-reviewed literature for global studies related to geological hazards. On the other hand, studies of earthquake and tsunami risk are now using stochastic modelling approaches to allow for a fully probabilistic assessment of risk, which could benefit the modelling of risk from other hazards. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales. Through this paper, we hope to encourage further dialogue on knowledge sharing between disciplines and communities working on different hazards and risk and at different spatial scales.

Abstract. Frequency and intensity of gust wind speeds associated with severe mid-latitude winter storms are estimated by applying extreme value statistics to data sets from regional climate models (RCM). Maximum wind speeds related to probability are calculated with the classical peaks over threshold method, where a statistical distribution function is fitted to the reduced sample describing the tail of the distribution function. From different sensitivity studies it is found that the Generalized Pareto Distribution in combination with a Maximum-Likelihood estimator provide the most reliable and robust results.For a reference period from 1971 to 2000, the ability of the RCMs to realistically simulate extreme wind speeds is investigated. For this purpose, data from three RCM scenarios, including the REMO-UBA simulations at 10 km resolution and the so-called consortial runs performed with the CCLM at 18 km resolution (two runs), are evaluated with observations and a pre-existing storm hazard map for Germany. It is found that all RCMs tend to underestimate the magnitude of the gusts in a range between 10 and 30% for a 10-year return period. Averaged over the investigation area, the underestimation is higher for CCLM compared to REMO. The spatial distribution of the gusts, on the other hand, is well reproduced, in particular by REMO.

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