This paper proposes a heat-wave definition for Bangladesh that could be used to trigger preparedness measures in a heat early warning system (HEWS) and explores the climate mechanisms associated with heat waves. A HEWS requires a definition of heat waves that is both related to human health outcomes and forecastable. No such definition has been developed for Bangladesh. Using a generalized additive regression model, a heat-wave definition is proposed that requires elevated minimum and maximum daily temperatures over the 95th percentile for 3 consecutive days, confirming the importance of nighttime conditions for health impacts. By this definition, death rates increase by about 20% during heat waves; this result can be used as an argument for public-health interventions to prevent heat-related deaths. Furthermore, predictability of these heat waves exists from weather to seasonal time scales, offering opportunities for a range of preparedness measures. Heat waves are associated with an absence of normal premonsoonal rainfall brought about by anomalously strong low-level westerly winds and weak southerlies, detectable up to approximately 10 days in advance. This circulation pattern occurs over a background of drier-than-normal conditions, with below-average soil moisture and precipitation throughout the heat-wave season from April to June. Low soil moisture increases the odds of heat-wave occurrence for 10–30 days, indicating that subseasonal forecasts of heat-wave risk may be possible by monitoring soil-moisture conditions.

Climate resilience is increasingly prioritized by international development agencies and national governments. However, current approaches to informing communities of future climate risk are problematic. The predominant focus on end-of-century projections neglects more pressing development concerns, which relate to the management of shorter-term risks and climate variability, and constitutes a substantial opportunity cost for the limited financial and human resources available to tackle development challenges. When a long-term view genuinely is relevant to decisionmaking, much of the information available is not fit for purpose. Climate model projections are able to capture many aspects of the climate system and so can be relied upon to guide mitigation plans and broad adaptation strategies, but the use of these models to guide local, practical adaptation actions is unwarranted. Climate models are unable to represent future conditions at the degree of spatial, temporal, and probabilistic precision with which projections are often provided, which gives a false impression of confidence to users of climate change information. In this article, we outline these issues, review their history, and provide a set of practical steps for both the development and climate scientist communities to consider. Solutions to mobilize the best available science include a focus on decision-relevant timescales, an increased role for model evaluation and expert judgment and the integration of climate variability into climate change services. This article is categorized under: Climate and Development > Knowledge and Action in Development K E Y W O R D S climate change adaptation, climate change projections, climate resilience, climate services, international development

Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

East Africa experiences chronic food insecurity, with levels varying from year-to-year across the region. Given that much can be done to prevent this level of suffering before it happens, humanitarian agencies monitor early indicators of food insecurity to trigger early action. Forecasts of total seasonal rainfall are one tool used to monitor and anticipate food security outcomes. Factors beyond rainfall, such as conflict, are key determinants of whether lack of rainfall can become a problem. In this paper, we present a quantitative analysis that isolates the value of rainfall information in anticipating food security outcomes across livelihood groups in East Africa. Comparing observed rainfall and temperature with food security classifications, we quantify how much the chance of food insecurity increases when rainfall is low. Results differed dramatically among livelihood groups; pastoralists in East Africa more frequently experience food insecurity than do non-pastoralists, and 12 months of low rainfall greatly increases the chances of Bcrisis^and Bemergency^food security in pastoralist regions. In non-pastoralist regions, the relationship with total rainfall is not as strong. Similar results were obtained for livelihood groups in Kenya and Ethiopia, with slightly differing results in Somalia. Given this, we evaluated the relevance of monitoring and forecasting seasonal total rainfall. Our quantitative results demonstrate that six months of rainfall observations can already indicate a heightened risk of food insecurity, a full six months before conditions deteriorate. Combining rainfall observations with seasonal forecasts can further change the range of possible outcomes to indicate higher or lower risk of food insecurity, but the added value of seasonal forecasts is noticeable only when they show a strong probability of below-normal rainfall.