Recent special reports on climate extremes have shown evidences of changes in the patterns of climate extremes at global, regional and local scales. Understanding the characteristics of climate extremes at regional and local levels is critical not only for the development of preparedness and early warning systems, but is also fundamental in the development of any adaptation strategies. There is still very limited knowledge regarding the past, present and future patterns of climate extremes in the Greater Horn of Africa (GHA). This study, which was supported by the World Bank Global Facility for Disaster Reduction and Recovery (WB-GFDRR) and implemented by the World Meteorological Organization, was organized in terms of three workshops with three main objectives; (1) analysis of daily rainfall and temperature extremes for ten countries in the GHA region using observed in situ data running from 1971 to 2006, (2) assessing whether the United Kingdom Met-office and Hadley centre Providing REgional Climates for Impact Studies (UK-PRECIS) modelling system can provide realistic representation of the past and present climate extremes as observed by available in situ data, and (3) studying the future regional climate extremes under different scenarios to further assess the expected changes in climate extremes. This paper, therefore, uses the outputs of these workshops and also includes post-workshop analyses to assess the changes of climate extremes within the GHA. The results showed a significant decrease in total precipitation in wet days greater than 1 mm and increasing warm extremes, particularly at night, while cold extremes are decreasing. Considering a combination of geophysical models and satellite gravimetry observations from the Gravity Recovery and Climate Experiment (GRACE) mission in the frame of GRACE daily Kalman-smoothing models, for the years 2002 to 2010, we explored a decline in total water storage variations over the GHA.

The Long-Rains wet season of March-May (MAM) over Kenya in 2018 was one of the wettest on record. This paper examines the nature, causes, impacts, and predictability of the rainfall events, and considers the implications for flood risk management. The exceptionally high monthly rainfall totals in March and April resulted from several multi-day heavy rainfall episodes, rather than from distinct extreme daily events. Three intra-seasonal rainfall events in particular resulted in extensive flooding with the loss of lives and livelihoods, a significant displacement of people, major disruption to essential services, and damage to infrastructure. The rainfall events appear to be associated with the combined effects of active Madden-Julian Oscillation (MJO) events in MJO phases 2-4, and at shorter timescales, tropical cyclone events over the southwest Indian Ocean. These combine to drive an anomalous westerly low-level circulation over Kenya and the surrounding region, which likely leads to moisture convergence and enhanced convection. We assessed how predictable such events over a range of forecast lead times. Long-lead seasonal forecast products for MAM 2018 showed little indication of an enhanced likelihood of heavy rain over most of Kenya, which is consistent with the low predictability of MAM Long-Rains at seasonal lead times. At shorter lead times of a few weeks, the seasonal and extended-range forecasts provided a clear signal of extreme rainfall, which is likely associated with skill in MJO prediction. Short lead weather forecasts from multiple models also highlighted enhanced risk. The flood response actions during the MAM 2018 events are reviewed. Implications of our results for forecasting and flood preparedness systems include: (i) Potential exists for the integration of sub-seasonal and short-term weather prediction to support flood risk management and preparedness action in Kenya, notwithstanding the particular challenge of forecasting at small scales. (ii) We suggest that forecasting agencies provide greater clarity on the difference in potentially useful forecast lead times between the two wet seasons in Kenya and East Africa. For the MAM Long-Rains, the utility of sub-seasonal to short-term forecasts should be emphasized; while at seasonal timescales, skill is currently low, and there is the challenge of exploiting new research identifying the primary drivers of variability. In contrast, greater seasonal predictability of the Short-Rains in the October-December season means that greater potential exists for early warning and preparedness over longer lead times. (iii) There is a need for well-developed Atmosphere 2018, 9, 472 2 of 30 and functional forecast-based action systems for heavy rain and flood risk management in Kenya, especially with the relatively short windows for anticipatory action during MAM.

The 2019 October–December rains over East Africa were one of the wettest seasons on record, with many locations receiving more than double the climatological rainfall, leading to floods and landslides. The wet conditions were associated with the positive Indian Ocean Dipole event, with warm sea surface temperatures in the western Indian Ocean. Seasonal forecasts correctly predicted above average rainfall during the season. Climate model projections suggest that such events may become more frequent under future climate change.

Eastern Africa exhibits bimodal rainfall consisting of long rains (March-May) and short rains (October-December), changes in which have profound socioeconomic and environmental impacts. In this Review, we examine the drivers and corresponding impacts of Eastern African rainfall variability. Remote teleconnections, namely the El Niño-Southern Oscillation and the Indian Ocean Dipole, exert a dominant influence on interannual variability. From the mid-1980s to 2010, the long rains have tended toward a drier state (trends of −0.65 to −2.95 mm season −1 year −1 ), with some recovery thereafter, while the short rains have become wetter since the mid 1980s (1.44 to 2.36 mm season −1 year −1 ). These trends, overlain by substantial year-to-year variations, affect the severity and frequency of extreme flooding and droughts, the stability of food and energy systems, the susceptibility to water-borne and vectorborne diseases, and ecosystem stability. Climate model projections of rainfall changes differ, but there is some consensus that the short rains will deliver more rainfall than the long rains by 2030-2040, with implications for sustaining agricultural yields and triggering climaterelated public health emergencies. Mitigating the impacts of future Eastern African climate requires continued investments in agriculture, clean water, medical and emergency infrastructures, and development and adoption of adaptation strategies, as well as targeted early-warning systems driven by improved meteorological observations. Sections 2015 coincided with a weaker IOD, producing anomalies ~50% above the climatological mean 18 . However, these relationships are nonlinear, as demonstrated by extreme 2019/2020 rainfall that occurred during an anomalously positive phase of the IOD but neutral ENSO conditions 20 .The IOD and ENSO physically influence Eastern African short rains by modifying regional atmospheric circulation features (Fig. 2a,b).Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author selfarchiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.