Forecasts on subseasonal (SS) timescales have become increasingly vital in bridging the gap between medium-range weather and long-range seasonal forecasts. The East African region is a 'sweetspot' for SS predictability, offering strong potential for these forecasts to inform critical agricultural decisions, such as determining the optimal planting times based on the onset of the rainy season. However, knowledge of rainy season onset predictability at this timescale is currently limited. In this paper, we evaluate the predictability of weekly rainfall and rainy season onset over East Africa (EA) at SS timescales in the ECMWF extended range forecast model. The results show that weekly rainfall forecasts have strong predictive skill up to 4 weeks ahead, with the 'long rains' March-May (MAM) season having higher skill than the 'short rains' October-December (OND). The latest ECMWF model cycle (C48) shows improvements in skill over the previous versions. Subsetting SS weekly forecasts by MJO phases does not produce markedly different results, suggesting that other drivers play a stronger role in shaping SS predictability. SS forecasts of onset dates have notable skill at 2-3 weeks' lead time, with high correlation and 'hit rates' (HRs) for forecasts of anomalously early and late-onset years. However, mean absolute errors in onset date forecasts remain high, typically similar to 6-12 days. This underscores the challenges of predicting specific onset dates and the need for less stringent metrics, such as broader onset 'windows'. Broader onset windows improve forecast accuracy, though a +/- 3-day window achieves HRs < 0.5. Analysis of case study seasons of anomalous onset illustrates the operational value of SS onset forecasts in informing agricultural decisions. The study highlights the potential of integrating SS forecasts into a seamless seasonal-to-subseasonal (S2S) framework to support agricultural decision-making and resilience through a 'Ready-Set-Go' system. It also reinforces the need to enhance co-production with agricultural stakeholders in determining and evaluating appropriate onset metrics for actionable information to take advantage of the high SS predictability.

We examine how smallholder farmers plan and make decisions around the onset of the rainy season, and how these decisions are shaped by experiences of onset variability and access to climate information. The study further explores how seamless subseasonal onset forecasts could support decision-making and assesses whether the provision of forecast skill information can enhance their usability. Specifically, we identify tolerable forecast skill levels that stakeholders consider acceptable for informing critical onset-related decisions.Findings show that onset timing is highly variable across years, strongly influencing key farm-level decisions such as planting time. Despite the availability of early warning advisories, uptake remains low, with many farmers relying on observed rainfall. As a result, planting decisions remain largely reactive, with farmers often waiting for multiple days of rainfall before acting.Onset-related decision-making varies by farm size and risk tolerance. Larger scale farmers are more likely to take higher risks, while smallholder farmers are cautious, contributing to high spatial variability in planting dates.Across farm and institutional levels, decision-making depends on both the availability of onset forecasts and its perceived reliability. Forecast updates indicating early or delayed onset influence preparatory activities, although not all decisions are equally sensitive. High-stakes decisions, such as planting time and input acquisition, require high forecast accuracy. However, current early warning systems often lack sufficient lead time and do not communicate forecast skill.The study highlights a gap between forecast provision and decision needs and underscores the potential of seamless SS onset forecasts to support more proactive agricultural decision-making.

Compared to prolonged seasonal droughts, impactful within-season agrometeorologically relevant dry spells of a relatively shorter duration that lie within the subseasonal-to-seasonal (S2S) timescale can have significant negative impacts on agriculture and pose severe threats to food security, particularly in agro-based economies such as those in Southern Africa. This study assesses the skill of the ECMWF ensemble subseasonal forecasting system in predicting these impactful events during the austral summer maize growing season (October to March, ONDJFM). We utilise two definitions of agrometeorologically relevant dry spells: (1) a generic index based on accumulated daily precipitation falling below optimal thresholds for the maize crop across the broader Southern Africa domain, and (2) case-specific events where a crop yield proxy derived over Zimbabwe’s primary maize-growing region falls below critical thresholds. Our findings reveal that predictive skill for the generic index declines with longer lead times but remains higher during the OND sub-season compared to JFM. Furthermore, the ECMWF subseasonal forecasting system demonstrates predictive skill for these events with a 10-to-30-day lead-time, particularly in northern Zimbabwe, central Zambia, Malawi, and northern Mozambique. For the case-specific events, findings reveal that prediction accuracy is conditioned by the model’s ability to simulate key atmospheric circulation patterns that modulate such extreme events. Overall, these results underscore the potential of the ECMWF subseasonal forecasting system to improve drought early warning systems and support anticipatory action initiatives that are still in their infancy in Southern Africa.

This article pioneers a unique approach to examining generic dry spells, shifting focus from traditional rain‐free period analysis to a crop‐centric perspective that integrates an anticipatory lens inspired by Impact‐based Forecasting (IbF). Moving beyond traditional analyses of rain‐free periods, the article evaluates these impactful within‐season large‐scale agrometeorologically relevant dry spells (LARDS) not by the number of days with minimal or no rainfall but by their impact—specifically, the adequacy of root‐zone soil moisture to meet the optimal requirements of maize crops, as quantified through the Water Requirement Satisfaction Index (WRSI). LARDS were identified in maize‐intensive growing regions of Zimbabwe under two maize planting date scenarios: meteorology‐guided and uninformed. The research characterizes impactful within‐season LARDS occurring at sub‐seasonal to seasonal timescales over 36 years (1983–2018). Findings show that meteorological guidance improves yields while neglecting it results in lower yields. During LARDS, a distinct northwest‐to‐southeast suppressed rainfall pattern emerges over Zimbabwe, extending into neighbouring countries. This pattern is associated with a southwestward or northeastward displacement of Tropical Temperate Troughs (the regional primary rainfall system) relative to the country's location. Furthermore, LARDS exhibit overarching anticyclonic conditions impeding vertical cloud development with notable changes in the key local large‐scale mean climatic features influencing Southern Africa's weather. Specifically, the Mozambique Channel Trough, Angola Tropical Low, Saint Helena High and Mascarene High weaken anomalously, while the Botswana High strengthens during LARDS. Additionally, we demonstrate that LARDS have a northeastward propagation and have atmospheric signatures indicative of being triggered by upstream Rossby waves originating from the south coast of South America.

The Greater Horn of Africa is prone to extreme climatic conditions, thus, making climate services increasingly important in supporting decision-making processes across a range of climate sensitive sectors. This study aims to provide a comprehensive review of the recent advances, gaps and challenges in the provision of climate services over the region, for each of the components of the Global Framework for Climate Services. The study explores various milestones that have been achieved toward climate service delivery. The achievements include improvement of station network coverage, and enhancing the capacity of member states to utilize various tools in data analysis and generate routine climate products. The advancement in science, and availability of High-Performance Computing has made it possible for forecast information to be provided from nowcasting to seasonal timescales. Moreover, operationalizing of the objective forecasting method for monthly and seasonal forecasts has made it possible to translate tercile forecasts for applications models. Additionally, innovative approaches to user engagement through co-production, communication channels, user-friendly interfaces, and dissemination of climate information have also been developed. Despite the significant progress that has been made in the provision of climate services, there are still many challenges and gaps that need to be overcome in order to ensure that these services are effectively meeting the needs of users. The research of the science underpinning climate variability, capacity building and stakeholder engagement, as well as improved data management and quality control processes are some of the gaps that exist over the region. Additionally, communication and dissemination of climate information, including timely warnings and risk communication, require improvement to reach diverse user groups effectively. Addressing these challenges will require strengthened partnerships, increased investment in capacity building, enhanced collaboration between the climate information producers and stakeholders, and the development of user-friendly climate products. Bridging these gaps will foster greater resilience to climate-related hazards and disasters in the Greater Horn of Africa and support sustainable development in the region.

Study area Tana River Basin in Kenya. Study Focus Flood-related impacts and losses have been rising. Therefore, understanding flood characteristics, drivers, and predictability is critical for informed decisions in the ongoing flood early warning (FldEWS) projects. This study presents an in-depth analysis of hydro-meteorological, Sentinel Mission (SM), and ensemble hydrological model datasets. We examine flood characteristics using observed hydro-meteorological and SM datasets, followed by statistical analysis of climate drivers of flood events at inter-annual and sub-seasonal (S2S) time scales. Finally, reforecasts from Global Flood Awareness System (GloFAS) are assessed against observed river flows. New hydrological insights for the study region There is a high inter-annual variability of flood events with flood peaks occurring in May and December. SM satellites have the ability to map flooded areas in near-real time. At inter-annual timescales, positive Indian Ocean Dipole (IOD) and warm El Niño Southern Oscillation (ENSO) drives short rains (October to December). At Sub-Seasonal (S2S) timescales, Madden Julian Oscillation (MJO; phases 2-4) is a notable driver of flood related extreme rainfall. GloFAS offers reliable forecasts depending on the flood magnitude, trigger probability, and 'anticipation window' and it meets the tolerable skill requirements for flood preparedness actions (FAR < 50% and POD > 50%) with up to a 20-day lead time for 1 and 2-year return periods. We subsequently discuss how our research findings can inform the development of FldEWS in Kenya, with an emphasis on improved co-production of flood forecast information with relevant stakeholders.

Over the East Africa region forecasts of the onset of the rainy seasons have the potential to support decision-making, especially in the largely rain-fed agricultural sector. However, the understanding of key features of onset remains limited. Here, we analyse the variability of onset and associated drivers at interannual and subseasonal timescales, using several onset definitions. Results show that the onset date is especially variable from year to year in some of the high-potential agricultural areas (standard deviation >20 days), which has implications for agricultural risk management. The choice of onset definition metric matters; agronomic definitions have limited applicability at the regional scale and are also highly sensitive to the spatial scale of analysis and to the choice of rainfall data. Onset information provided at coarse scales should be used with caution for decision-making at the local scale; the "hit rate" of coarse-scale tercile onset information at the local scale is less than 40% on average. To varying degrees, onset is related to total seasonal rainfall and thus to dominant interannual drivers of rainfall, including the Indian Ocean Dipole and ENSO modes in October-December and the western Pacific "V-gradient" pattern in March-May. However, by analysing the dominant proportion of onset variance unrelated to total rainfall during the climatological season we show a substantial influence of subseasonal drivers, notably the Madden-Julian Oscillation. As such, there is an opportunity for rainfall onset information to be provided across seasonal and subseasonal timescales. Our work reinforces the need for enhanced co-production of such onset information with stakeholders, especially regarding the choice of metric, alignment of forecasts with livelihood calendars, interpretation of the credibility of information content for local-level decision-making, as well as appropriate strategies for staggered risk management interventions informed by forecasts over "seamless" lead times.

Kenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end-to-end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a 'sweetspot' for rainfall predictability at sub-seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances.

Southern Africa relies heavily on precipitation for agricultural purposes; therefore, spatial and temporal changes in precipitation are crucial to identify and understand. The South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the southwest Indian Ocean, is evaluated in future projections. Using a best-fit algorithm, future projections of the SIOCZ are determined, which indicate a northward shift of approximately 120 km in CMIP5 models under RCP8.5. A dipole pattern of precipitation wetting/drying is evident, where wetting occurs to the north of the climatological axis of maximum rainfall, implying a northward shift of the Inter Tropical Convergence Zone, consistent with the SIOCZ shift. Common drivers responsible for model changes include enhanced warming in the northern Indian Ocean in line with the ‘warmest-get-wetter’ sea surface temperature hypothesis, which impacts circulation by transporting moisture away from the SIOCZ towards the equator. Most CMIP5 models exhibit drying trends over the SIOCZ region, with mechanisms driving uncertainty related to diverse warming trends across models. Empirical orthogonal function patterns of future precipitation changes across CMIP5 models exhibit a pattern much like the SIOCZ, which is related to inter-model changes in future temperature changes. Reductions in model spread are established in SIOCZ projections, whereby model processes of change exhibit agreement, despite differing initial SIOCZ conditions. Therefore, model process convergence and coherence are established with respect to projected changes in the SIOCZ, irrespective of initial climatology biases. Understanding future changes in this feature will help inform decision-making for water and agriculture adaptation planning in southern Africa.

Water resource system planning is complicated by uncertainty on the magnitude and direction of climate change. Therefore, developments such as new infrastructure or changed management rules that would work acceptably well under a diverse set of future conditions (i.e., robust solutions) are preferred. Robust multi-objective optimisation can help identify advantageous system designs which include existing infrastructure plus a selected subset of new interventions. The method evaluates options using simulated water resource performance metrics statistically aggregated to summarise performance over the climate scenario ensemble. In most cases such ‘robustness metrics’ are sensitive to scenarios under which the system performs poorly and so results may be strongly influenced by a minority of unfavorable climate scenarios. Understanding the influence of specific climate scenarios on robust optimised decision alternatives can help better interpret their results. We propose an automated multi-criteria design-under-uncertainty sensitivity analysis formulation that uses multi-objective evolutionary algorithms to reveal robust and efficient designs under different samples of a climate scenario ensemble. The method is applied to a reservoir management problem in the Rufiji River basin, Tanzania, which involves the second largest dam in Africa. We find that solutions optimised for robustness under alternative groups of climate scenarios exhibit important differences. This becomes particularly decision-relevant if analysts and/or decision-makers have differing confidence levels in the relevance of certain climate scenarios. The proposed approach motivates continued research on how climate model credibility should inform climate scenario selection because it demonstrates the influence scenario selection has on recommendations arising from robust optimisation design processes.

Skillful weather and climate forecasts, if utilized effectively, have the potential to improve preparedness and disaster risk reduction. Forecast-based Action (FbA) is a framework for aiding decisions on preparedness in advance of weather/climate hazards, through use of forecasts. Here, we present a summary of research results and pilot project work within the Arid and Semi-Arid Land (ASAL) areas of Kenya conducted under the Towards Forecast-based Preparedness and Action (ForPAc) project. We also present opportunities for scaling up FbA across the Greater Horn of Africa region through leveraging on connected projects and initiatives like Down2Earth. Skill assessment of a pool of weather/climate models has established the most skilful multi-model combinations for monthly-seasonal timescale. Co-production initiatives between forecast users and producers established the forecast variables best aligned with Kenya’s existing Drought Early Warning Systems (DEWS); Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI) and soil moisture, as well as optimum forecast delivery time required by the DEWS processes. Our analysis shows that rainfall forecasts have skill across ‘seamless’ sub-seasonal to seasonal lead times, offering the potential to improve the anticipatory actions within the DEWS of Kitui county of Kenya. Working with multiple stake-holders from across local and national government, humanitarian agencies, forecasting services and climate researchers, we have explored the potential for a more anticipatory, proactive DEWS using forecast information. The Down2Earth project, which aims at translating climate information for adaptation and climate-resilience across decision-making levels is leveraging on gains of ForPAc by advancing FbA approaches within the rural communities of Kenya, Somalia and Ethiopia. To facilitate the institutionalization of FbA, we have developed a regional roadmap to guide implementation within National, regional and international humanitarian actors.

The lives and livelihoods of people around the world are increasingly threatened by climate-related risks as climate change increases the frequency and severity of high-impact weather. In turn, the risk of multiple hazards occurring simultaneously grows and compound impacts become more likely. The World Meteorological Organization (WMO) proposed the use of multi-hazard impact-based forecasting (IbF) to better anticipate and reduce the impacts of concurrent hazards, but as yet, there are few operational examples in the humanitarian sector. Drought is particularly susceptible to multi-hazard influences. However, challenges encountered in the development of drought IbF systems – including poor understanding of compound impacts and specific hazard-focused mandates – raise important questions for the feasibility of multi-hazard IbF as envisioned by the WMO. With these challenges in mind, we propose an interim approach in which real-time assessment of dynamic vulnerability provides a context for drought-based IbF. The incorporation of dynamic vulnerability indicators account for the local effects of non-drought hazards, whilst the use of a drought-based system facilitates effective intervention. The proposed approach will improve our understanding of compound events, enhance adoption of IbF in the humanitarian sector, and better mitigate the impacts of concurrent hazards.

Subseasonal-to-seasonal (S2S) forecasts are bridging the gap between weather forecasts and long-range predictions. Decisions in various sectors are made in this forecast timescale, therefore there is a strong demand for this new generation of predictions. While much of the focus in recent years has been on improving forecast skill, if S2S predictions are to be used effectively, it is important that along with scientific advances, we also learn how best to develop, communicate and apply these forecasts. In this presentation, we present recent progress in the applications of S2S forecasts. We summarise case studies from a recently-published applications community review paper in the Bulletin of the American Meteorological Society (BAMS), covering sectoral applications of S2S predictions from around the world, including public health, disaster preparedness, water management, telecommunications, energy and agriculture. Involving over 60 authors and drawing from the recent advances and experience of researchers and users working with S2S forecasts globally, we explore the value of applications-relevant S2S predictions through a series of sectoral cases where uptake is starting to occur. From across 12 case studies, we show that: * The S2S forecasting timescale is a new concept for many users. While the additional value of S2S forecasts for decision-making is increasingly gaining interest among users, incorporating probabilistic ensemble S2S forecasts into existing operations is not trivial. * Barriers to widespread adoption of S2S forecasts include lack of access to the forecasts and the co-production to tailor forecasts to user needs, as well as varying ‘in house’ expertise in how to interpret and effectively apply them. This can create a ‘knowledge-value’ gap in some instances. * S2S forecasts do not produce a ‘go/no go’ answer of how a user should respond to a potential hazard; instead they provide additional, supplementary ‘situational awareness’ information that can be used to support decision-making on S2S timescales. While S2S forecasting is still a maturing discipline globally, this publication marks a significant step forward in moving from potential to actual S2S forecasting applications – a collective body of evidence demonstrating both skill and utility across sectors that places user needs and applications at the forefront of S2S forecast development. Our paper, ‘Advances in the application and utility of subseasonal-to-seasonal predictions’, is available from BAMS as an open access publication: https://doi.org/10.1175/BAMS-D-20-0224.1.

Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.

Drought and food security crises heighten risks to lives and livelihoods in East Africa. In recent years, a shift towards acting in advance of such events has gained momentum, notably among the humanitarian and development community. This shift is premised on tools that link climate forecasts with pre-agreed actions and funding, known as Forecast-based Action (FbA), or anticipatory action more widely. While FbA approaches have been developed by a number of humanitarian agencies, the key to scaling-up is mainstreaming these approaches into national risk management systems. This paper addresses this gap in the context of drought risk management in Kenya. We analyse Kenya's current drought management system to assess the potential usability of climate forecast information within the existing system, and outline steps towards improved usability of climate information. Further, we note the critical importance of enabling institutions and reliable financing to ensure that information can be consistently used to trigger early action. We discuss the implications of this for scaling-up FbA into national risk management systems.

The need to assess major infrastructure performance under a changing climate is widely recognized yet rarely practiced, particularly in rapidly growing African economies. Here, we consider high-stakes investments across the water, energy, and food sectors for two major river basins in a climate transition zone in Africa. We integrate detailed interpretation of observed and modeled climate-system behavior with hydrological modeling and decision-relevant performance metrics. For the Rufiji River in Tanzania, projected risks for the mid-21st century are similar to those of the present day, but for the Lake Malawi-Shire River, future risk exceeds that experienced during the 20th century. In both basins a repeat of an early-20th century multi-year drought would challenge the viability of proposed infrastructure. A long view, which emphasizes past and future changes in variability, set within a broader context of climate-information interpretation and decision making, is crucial for screening the risk to infrastructure.

The ‘silent revolution’ of numerical weather prediction (NWP) has led to significant social benefits and billions of dollars in economic benefits to mid-latitude countries, however the level of benefit in sub-Saharan Africa has been very limited, despite the potential to save lives, improve livelihoods, protect property and infrastructure and boost economies. Ongoing climate change in Africa, and the associated projected intensification of weather impacts in coming decades, makes the realisation of effective and more reliable weather forecasts and climate services even more urgent. It is widely recognised that to achieve this potential, investment is required in strengthening decision makers’ understanding of weather predictions and confidence in interpreting and appropriately applying forecasts, alongside transparent communication of the levels of skill and probability or certainty in forecast products. However, on all time scales of prediction, it is generally unrecognised that many forecasts that produce user-relevant metrics have such low skill that they are only marginally valuable to stakeholders, creating significant practical and ethical barriers to increasing uptake and generating benefits. Here, we present substantial evidence that even a modest investment in science for weather information and forecast techniques, to provide new technology and tools for Africa, can significantly increase the skill of user-relevant forecast products on all time scales. This will be a necessary enabler for building trust in and uptake of decision-relevant forecasts with the potential to deliver significant social and economic benefits. We present here an argument that incremental improvements in the skill of weather forecasting across all timescales in the African tropics, alongside strengthening communication and understanding of these forecasts, is fundamental to saving lives and enhancing livelihoods. Investing in the capacity and capability of National Meteorological Services and research institutions is essential to ensure lifesaving and life-enhancing services continue to be developed with and designed to serve the populations of sub-Saharan countries.

Acting in advance of floods, drought and cyclones often requires decision-makers to work with weather forecasts. The inherently probabilistic nature of these forecasts can be problematic when deciding whether to act or not. Cost-loss analysis has previously been employed to support forecast based decision-making such as Forecast-based Financing (FbF), providing insight to when an FbF system has 'potential economic value' relative to a no-forecast alternative. One well-known limitation of cost-loss analysis is the difficulty of estimating losses (which vary with hazard magnitude and extent, and with the dynamics of population vulnerability and exposure). A less-explored limitation is ignorance of the temporal dynamics (sequencing) of costs and losses. That is, even if the potential economic value of a forecast system is high, the stochastic nature of the atmosphere and the probabilistic nature of forecasts could conspire over the first few forecasts to increase the expense of using the system over the no-forecast alternative. Thus, for a forecast-based action system to demonstrate value, it often needs to be used over a prolonged length of time. However, knowing exactly how long it must be used to guarantee value is unquantified. This presents difficulties to institutions mandated to protect those at risk, who must justify the use of limited funds to act in advance of a potential, but not definite disaster, whilst planning multi-year strategies. Here we show how to determine the period over which decision makers must use forecasts in order to be confident of achieving 'value' over a no-forecast alternative. Results show that in the context of seasonal forecasting it is plausible that more than a decade may pass before a FbF system will have some certainty of showing value, and that if a particular user requires an almost-certain guarantee that using a forecast will be better than a no-forecast strategy, they must hold out until a near-perfect forecast system is available. The implication: there is potential value in seasonal forecasts, but to exploit it one must be prepared to play the long game.

There is a huge opportunity for the African continent to benefit from the ‘silent revolution’ in weather forecasting that has been realised in the mid-latitudes throughout the twentieth century. While there are tremendous societal and economic benefits from advancing the science behind weather forecasting in sub-Saharan Africa, there are also significant barriers to realising advances. This policy brief examines the value of investment in African weather forecasting science and the technical & communication challenges that this will bring with wider implementation.

AbstractThe need to stress test designs and decisions about major infrastructure under climate change conditions is increasingly being recognised. This chapter explores new ways to understand and—if possible—reduce the uncertainty in climate information to enable its use in assessing decisions that have consequences across the water, energy, food and environment sectors. It outlines an approach, applied in the Rufiji River Basin in Tanzania, that addresses uncertainty in climate model projections by weighting them according to different skill metrics; how well the models simulate important climate features. The impact of different weighting approaches on two river basin performance indicators (hydropower generation and environmental flows) is assessed, providing an indication of the reliability of infrastructure investments, including a major proposed dam under different climate model projections. The chapter ends with a reflection on the operational context for applying such approaches and some of the steps taken to address challenges and to engage stakeholders.