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Vb cyclones are major drivers of extreme precipitation and floods in the study area of hydrological Bavaria (Germany). When assessing climate change impacts on Vb cyclones, internal variability of the climate system is an important underlying uncertainty. Here, we employ a 50-member single-model initial-condition large ensemble of a regional climate model to study climate variability and forced change on Vb cyclones. An artificial neural network detects cutoff lows over central Europe, which are associated with extreme precipitation Vb cyclones. Thus, machine learning filters the large ensemble prior to cyclone tracking. Our results show a striking change in Vb seasonality with a strong decrease of Vb cyclones in summer (−52%) and a large increase in spring (+73%) under the Representative Concentration Pathway 8.5. This change exceeds the noise of internal variability and leads to a peak shift from summer to spring. Additionally, we show significant increases in the daily precipitation intensity during Vb cyclones in all seasons.Plain Language Summary Bavaria, a state in the southeast of Germany, has been hit by several devastating floods in recent decades triggered by a storm type called Vb. For future flood risk in Bavaria it is crucial to understand how climate change affects Vb storms. This study uses high-resolution climate simulations over Europe to study changes in the frequency of Vb storms, their seasonal occurrence, and their rainfall intensity under a high greenhouse gas concentration scenario. However, Vb storms are rare events and a single simulation may not provide enough events to distinguish between climate change and random, natural variations. Therefore, we employ a large database of 50 climate simulations with the same settings and greenhouse gas concentration scenario, but slightly different starting conditions, in order to robustly estimate climate change effects on Vb storms. The drawback of using 50 simulations is the high amount of data. Therefore, we apply machine learning for pattern recognition to detect the low-pressure systems related to extreme precipitation Vb storms in the climate simulations. Our results show that climate change considerably affects the seasonal occurrence of Vb storms with a shift from summer to spring. Furthermore, the daily rainfall intensity in Bavaria increases during Vb storms significantly with climate change.

The double cropping system has been widely applied in many subtropical viticultural regions. In the 2-year study of 2014–2015, four grape varieties were selected to analyze their fruit volatile compounds in four consecutive seasons in the Guangxi region of South China, which had a typical subtropical humid monsoon climate. Results showed that berries of winter seasons had higher concentrations of terpenes, norisoprenoids, and C6/C9 compounds in “Riesling,” “Victoria,” and “Muscat Hamburg” grapes in both of the two vintages. However, in the “Cabernet Sauvignon” grapes, only the berries of the 2014 winter season had higher terpene concentrations, but lower norisoprenoid concentrations than those of the corresponding summer season. The Pearson correlation analysis showed the high temperature was the main climate factor that affected volatile compounds between the summer and winter seasons. Hexanal, γ-terpinene, terpinen-4-ol, cis-furan linalool oxide, and trans-pyran linalool oxide were all negatively correlated with the high-temperature hours in all of the four varieties. Transcriptome analysis showed that the upregulated VviDXSs, VviPSYs, and VviCCDs expressions might contribute to the accumulations of terpenes or norisoprenoids in the winter berries of these varieties. Our results provided insights into how climate parameters affected grape volatiles under the double cropping system, which might improve the understanding of the grape berries in response to the climate changes accompanied by extreme weather conditions in the future.