Extratropical cyclones are important meteorological phenomena in the Mediterranean and are essential for local water supplies, yet they also pose significant hazards for the region as a result of extreme precipitation or wind events. Although they have been extensively studied using global and regional climate models, their spatial and temporal variability in the Late Holocene is poorly understood. Here, we study a 3350-year climatological simulation that allows us to characterize Mediterranean cyclones better and provides a baseline for more accurately assessing the long-Term effects of future climate change on Mediterranean cyclones. To analyse Mediterranean cyclone characteristics, we use a seamless transient simulation from 1500 BCE to 1850 CE produced by the Community Earth System Model (CESM) with a 6-hourly temporal and 1.9°×2.5° horizontal resolutions. We found that Mediterranean cyclones exhibit pronounced multi-decadal variability in the order of 5 % throughout the entire Late Holocene with respect to several cyclone-related properties. For the cyclone frequency, a weak statistical relationship is identified with the East Atlantic (EA), East Atlantic/Western Russia (EAWR), and Scandinavian (SCAN) modes of circulation. Composite analyses of the most extreme cyclones with respect to wind speed and precipitation indicate that cyclones in the central Mediterranean have the potential to grow more intense over their entire lifetime than cyclones in the eastern Mediterranean. This is especially true for extreme wind speed cyclones, implying that people in the central Mediterranean are potentially more exposed to hazards caused by extreme cyclones.

Grassland landscapes are important ecosystems in East Africa, providing habitat and grazing grounds for wildlife and livestock and supporting pastoralism, an essential part of the agricultural sector. Since future grassland availability directly affects the future mobility needs of pastoralists and wildlife, we aim to model changes in the distribution of key grassland species under climate change. Here we combine a global and regional climate model with a machine learning-based species distribution model to understand the impact of regional climate change on different key grass species. The application of a dynamical downscaling step allows us to capture the fine-scale effects of the region’s complex climate, its variability and future changes. We show that the co-occurrence of the analysed grass species is reduced in large parts of eastern Africa, and particularly in the Turkana region, under the high-emission RCP8.5 scenario for the last 30 years of the 21^st century. Our results suggest that future climate change will alter the natural resource base, with potentially negative impacts on pastoralism and wildlife in East Africa.