Understanding changes in extreme compound hazard events is important for climate mitigation and policy. By definition, such events are rare so robust quantification of their future changes is challenging. An approach is presented, for probabilistic modelling and simulation of climate model data, which is invariant to the event definition since it models the underlying weather variables. The approach is based on the idea of a 'moving window' in conjunction with Generalised Additive Models (GAMs) and Bayesian inference. As such, it is robust to the data size and completely parallelizable, while it fully quantifies uncertainty allowing also for comprehensive model checking. Lastly, Gaussian anamorphosis is used to capture dependency across weather variables. The approach results in probabilistic simulations to enable extrapolation beyond the original data range and thus robust quantification of future changes of rare events. We illustrate by application to daily temperature, humidity and precipitation from a regional climate model.

Building resilience to environmental change is an integral part of long-term climate adaptation planning and local policy. There is an increased understanding of the impact of climate change on global crop production however, little focus has been given to local adaptation pathways and rural smallholder community responses, especially regarding food security. It is becoming increasingly evident that local level decision-making plays a vital role in reducing vulnerability to environmental change. This research aimed to qualitatively investigate coping and adaptive strategies adopted by smallholder farming households to respond to the impacts of drought in rural KwaZulu-Natal, South Africa. Focus group discussions (n = 7) consisting of 5-9 participants and individual interviews (n = 9) using pre-tested topic guides, involving a total of 57 adults were conducted in rural areas of drought-affected districts: Msinga, Richmond and Umbumbulu of KwaZulu-Natal, in July 2018. The data were analysed using thematic analysis in NVivo 12. Thematic analysis identified three principal themes: 1. Perceived effects of droughts on the local food system and diets; 2. Current coping strategies; and 3. Enablers for successful adaptation. All sites reported a change in food consumption habits, with the majority perceiving drought to be the main driver behind a shift from vegetable-based to starch-based diets and decreased animal source food consumption. Only short-term coping strategies were implemented across the study sites. However, knowledge of long-term adaptation strategies existed but was unattainable to most respondents. Recommendations of perceived context-specific long-term adaptation strategies that could be used at a local scale were communicated by the respondents. However, they would need external help to actualize them. A need exists to support smallholder communities' short-term response methods to drought to achieve more holistic resilience and successful adaptation. Short-term adaptation strategies, if implemented alone, often have significant trade-offs with longer-term adaptation and building resilience. This study highlights the need for targeted, contextualised policy solutions to improve smallholder productivity during drought through a strategic combination of both short- and longer-term adaptation measures, i.e. short-term adaptation should be guided by a long-term adaptation strategy. Proper planning, including the use of climate scenarios combined with information on nutritional status, is needed to develop context-specific and transformative adaptation strategies. These strategies should aim to strengthen resilience at a local level and should be included as policy recommendations.

Global warming is anticipated to intensify the hydrological cycle. However, this is neither expected to be globally uniform nor is the relationship between temperature increase and rainfall intensities expected to be linear. The objective of this study is to assess changes in annual rainfall extremes, total annual precipitation, and their relationship in the larger Mediterranean region. We use an up-to-date ensemble of 33 regional climate simulations from the EURO-CORDEX initiative at 0.11° resolution. We analyse the significance of trends for 1951-2000 and 2001-2100 under a 'business-as-usual' pathway (RCP8.5). Our future projections indicate a strong north/south Mediterranean gradient, with significant, decreasing trends in the magnitude of daily precipitation extremes in the south and the Maghreb region (up to -10 mm/decade) and less profound, increasing trends in the north. Despite the contrasting future trends, the 50-year daily precipitation extremes are projected to strongly increase (up to 100%) throughout the region. The 100-year extremes, derived with traditional extreme value approaches from the 1951-2000 simulations, underestimate the magnitude of these extreme events in the 2001-2100 projections by 30% for the drier areas of the Mediterranean (200-500 mm average annual rainfall) and by up to 20-30% for the wetter parts of the region. These 100-year extremes can occur at any time in any Mediterranean location. The contribution of the wettest day per year to the annual total precipitation is expected to increase (5-30%) throughout the region. The projected increase in extremes and the strong reductions in mean annual precipitation in the drier, southern and eastern Mediterranean will amplify the challenges for water resource management.

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.