Subsistence farming in southern Africa is vulnerable to extreme weatherconditions. The yield of rain-fed agriculture depends largely onrainfall-related factors such as total seasonal rainfall, anomalous onsetsand lengths of the rainy season and the frequency of occurrence of dryspells. Livestock, in turn, may be seriously impacted by climatic stresswith, for example, exceptionally hot days, affecting condition, reproduction,vulnerability to pests and pathogens and, ultimately, morbidity andmortality. Climate change may affect the frequency and severity of extremeweather conditions, impacting on the success of subsistence farming. Apotentially interesting adaptation measure comprises the timely forecastingand warning of such extreme events, combined with mitigation measures thatallow farmers to prepare for the event occurring. This paper investigates howthe frequency of extreme events may change in the future due to climatechange over southern Africa and, in more detail, the Limpopo Basin using aset of climate change projections from several regional climate modeldownscalings based on an extreme climate scenario. Furthermore, the paperassesses the predictability of these indicators by seasonal meteorologicalforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF)seasonal forecasting system. The focus is on the frequency of dry spells aswell as the frequency of heat stress conditions expressed in the temperatureheat index. In areas where their frequency of occurrence increases in thefuture and predictability is found, seasonal forecasts will gain importancein the future, as they can more often lead to informed decision-making toimplement mitigation measures. The multi-model climate projections suggestthat the frequency of dry spells is not likely to increase substantially,whereas there is a clear and coherent signal among the models of an increasein the frequency of heat stress conditions by the end of the century. Theskill analysis of the seasonal forecast system demonstrates that there is apotential to adapt to this change by utilizing the weather forecasts, giventhat both indicators can be skilfully predicted for the December–Februaryseason, at least 2 months ahead of the wet season. This is particularly thecase for predicting above-normal and below-normal conditions. The frequencyof heat stress conditions shows better predictability than the frequency ofdry spells. Although results are promising for end users on the ground,forecasts alone are insufficient to ensure appropriate response. Sufficientsupport for appropriate measures must be in place, and forecasts must becommunicated in a context-specific, accessible and understandable format.
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