We analyze the potential effect of global warming levels (GWLs) of 1.5 • C and 2 • C above pre-industrial levels (1861−1890) on mean temperature and precipitation as well as intra-seasonal precipitation extremes over the Greater Horn of Africa. We used a large, 25-member regional climate model ensemble from the Coordinated Regional Downscaling Experiment and show that, compared to the control period of 1971−2000, annual mean near-surface temperature is projected to increase by more than 1 • C and 1.5 • C over most parts of the Greater Horn of Africa, under GWLs of 1.5 • C and 2 • C respectively. The highest temperature increases are projected in the northern region, covering most parts of Sudan and northern parts of Ethiopia, and the lowest temperature increases are projected over the coastal belt of Tanzania. However, the projected mean surface temperature difference between 2 • C and 1. 5 • C GWLs is higher than 0.5 • C over nearly all land points, reaching 0.8 • C over Sudan and northern Ethiopia. This implies that the Greater Horn of Africa will warm faster than the global mean.While projected changes in precipitation are mostly uncertain across the Greater Horn of Africa, there is a substantial decrease over the central and northern parts of Ethiopia. Additionally, the length of dry and wet spells is projected to increase and decrease respectively. The combined effect of a reduction in rainfall and the changes in the wet and dry spells will likely impact negatively on the livelihoods of people within the coastal cities, lake regions, highlands as well as arid and semi-arid lands of Kenya, Tanzania, Somalia, Ethiopia and Sudan. The probable impacts of these changes on key sectors such as agriculture, water, energy and health sectors, will likely call for formulation of actionable policies geared towards adaptation and mitigation of the impacts of 1.5 • C and 2 • C warming.

The assessment of the performance of the October to December (OND), 2019 rainfall season in Zanzibar (Unguja and Pemba) with reference to local downscaled Tanzania Meteorological Authority (TMA) forecast, and regional (Intergovernmental Authority on Development Climate Prediction and Application Center (IGAD-ICPAC) weather forecasts were assessed by comparing the long term average of OND rainfall data and previous OND rainfall seasons of 2016, 2017 and 2018 as well as extreme positive Indian Ocean Dipole (IOD) during OND seasons of 1961, 1994, 1997, 2006 and 2019 for Zanzibar. The study assessed zonal (u) and meridional (v) winds at 850 and 200 mb, monthly and dekadal sea surface temperature (SST); the Madden Julien Oscillations (MJO) forecast reports and the ocean heat content data. Both gridded and observed datasets were processed into dekadal, monthly and seasonal averages and then analysed. The results revealed that, based on the observations, above normal rainfall of 936 and 908 mm were reported at stations of Kisauni (Unguja) and Karume airport (Pemba) during 2019 OND season. This amount was the first and second ever recorded for the extreme positive IOD during OND seasons of

This study examines the effects of 1.5°C and 2°C global warming levels (GWLs) on intra-seasonal rainfall characteristics over the Greater Horn of Africa. The impacts are analysed based on the outputs of a 25-member regional multi-model ensemble from the Coordinated Regional Climate Downscaling Experiment project. The regional climate models were driven by Coupled Model Intercomparison Project Phase 5 Global Climate Models for historical and future (RCP8.5) periods. We analyse the three major seasons over the region, namely March-May, June-September, and October-December. Results indicate widespread robust changes in the mean intra-seasonal rainfall characteristics at 1.5°C and 2°C GWLs especially for the June-September and October-December seasons. The March-May season is projected to shift for both GWL scenarios with the season starting and ending early. During the June-September season, there is a robust indication of delayed onset, reduction in consecutive wet days and shortening of the length of rainy season over parts of the northern sector under 2°C GWL. During the October-December season, the region is projected to have late-onset, delayed cessation, reduced consecutive wet days and a longer season over most of the equatorial region under the 2°C GWL. These results indicate that it is crucial to limit the GWL to below 1.5°C as the differences between the 1.5°C and 2°C GWLs in some cases exacerbates changes in the intra-seasonal rainfall characteristics over the Greater Horn of Africa.

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This study aimed at understanding the impacts of the seasonal hydroclimatic variables on maize yield and developing of statistical crop model for future maize yield prediction over Tanzania. The food security of the country is basically determined by availability of maize. Unfortunately, agriculture over the country is mainly rain fed hence highly endangered by the detrimental consequences of climate change and variability. Observed climate data was acquired from Tanzania Meteorological Authority (TMA) and Maize yield data from Food and Agriculture Organization (FAO). The study used the Mann-Kendall test and Sen's slope for trend and magnitude detection in minimum, maximum temperature and rainfall at the 95% confidence level. The results have shown that rainfall is decreasing over the country and especially during the growing season but increasing during short rains season. Characteristics of seasonal climatic variables, cycle during growing period were linked to maize yield, and high (low) yield was reported during anomalous wet (dry) growing seasons. This portrays seasonal dependence of maize production. Statistical crop model was built by aggregating spatial regions that have statistically significant relation with maize yield. Results show that, 58.8% of yield variance is linked to seasonal hydroclimate variability. Rainfall emerged as the dominant predictor variable for maize yield since it accounts for 44.1% of yield variance. The modeled and observed yields exhibit statistically substantial relationship (r = 0.78) hence depicting high credence of the built statistical crop model. Also, the results revealed a decreasing trend in Maize yield with further Lessing trend is projected to proceed in the future. This calls for adaptation and implementation of appropriate regional measures to raise maize production in order to feed the burgeoning human population amidst climate change.