Climate variability and change in Uganda and the entire Great Horn of Africa are areas of concern due to their impacts on the economy that largely depends on rainfed agriculture (Limantol et al. 2016; Onyutha 2018; 2019). Uganda has witnessed extreme weather events manifesting as droughts and floods resulting from below and above-normal or heavy rainfall, respectively, in the past years (Mulinde et al. 2016; Ojara et al. 2020). The events had devastating impacts on community livelihoods leading to the displacement of people, loss of lives, and destruction of property. The recent incident is the flooding in late 2021 that significantly affected over 40,000 people (Reliefweb 2021). This among other weather and climate hazards slowdown socioeconomic development, threatening the realization of most sustainable development goals.
Uganda and the East African region at large experience two distinct rainfall seasons, the March to May (MAM ‘long rains’) and the September to November (SON ‘short rains’) seasons (Ayugi et al. 2016; Ongoma and Chen 2017; Ayugi et al. 2018; Ngoma et al. 2021a). The two coincide with the passage of the Intertropical Convergence Zone (ITCZ; Nicholson 2018). The former season is characterized by more rainfall than the latter, because the ITCZ moves faster over the region during SON than MAM. In addition, SON rainfall has been reported to exhibit higher inter-annual and inter-decadal variability compared to MAM (Hastenrath et al. 1993; Saji et al. 1999), owing to its linkage to global teleconnections such as the El-Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) (Nicholson and Kim 1997; Ogwang et al. 2015; Ngarukiyimana et al. 2017; Endris et al. 2019; Ngoma et al. 2021b).
Past studies have reported a decreasing (increasing) trend in MAM (SON) rainfall (Camberlin et al. 2003; Onyutha 2016; Nsubuga et al. 2014, 2017; Ongoma and Chen 2017; Ngoma et al. 2021b). The drying trend in the MAM season has been attributed to anthropogenic processes and internal climate variability such as ENSO and Interdecadal Pacific Oscillation (Lyon 2014; Tierney et al. 2015). On the other hand, the wetting pattern for the short rains is linked to the large-scale weakening of the Walker circulation (Williams and Funk 2011; Liebmann et al. 2014; Tierney et al. 2015). Contrary to the current climatic condition which is characterized by several drought events across East Africa, a number of studies using model simulations show that the current is wet and will get wetter in future, a situation that has been termed as the ‘East African climate paradox’ (Rowell et al. 2015; Ongoma et al. 2018). This paradox has confused policy makers on what ought to be considered in preparing for future changes in rainfall patterns.
General circulation models (GCMs) are used to project climate patterns as they give an insight into the Earth’s likely response to warming induced by anthropogenic greenhouse gas (GHG) emissions (Stouffer et al. 2016). One set of GCMs is the Coupled Model Intercomparison Project (CMIP) outputs (Gates et al. 1999), used widely in climatological and hydrological studies. CMIP outputs have evolved over the years, with the latest being phase six (CMIP6) (Eyring et al. 2016). Future changes in the CMIP6 data gives a range of future GHG and land use change scenario estimated from integrated assessment models and based on various assumptions regarding economic growth, climate mitigation efforts, and global governance (Riahi et al. 2017). Recent studies that have utilized CMIP6 reported that the models exhibit improvements compared to CMIP5 (Akinsanola et al. 2020, 2021; Luo et al. 2020; Almazroui et al. 2020; Zhu et al. 2020; Ayugi et al. 2021).
Over Africa, Almazroui et al. (2020) employed 27 GCMs of CMIP6 under three emission scenarios and demarcated the region under 8 zones as recommended by the Intergovernmental Panel on Climate Change (IPCC). The study projected an increase in rainfall over East Africa, where Uganda is located. However, the study considered a broader domain that may not clearly picture the local climate changes. Past studies conducted over East Africa, including Tierney et al. (2015) and Ongoma et al. (2018), based on CMIP5, projected an increase in rainfall which will be higher in the short rains than the long rains. Tierney et al. (2015) further reported a likely decrease in June to August (JJA) rainfall. A recent study based on CMIP6 over the East African region focused mainly on the evaluation of CMIP6 models in simulating mean or extremes rainfall (Akinsanola et al. 2021; Makula and Zhou 2021; Ngoma et al. 2021c) or projections of precipitation extremes over broader East Africa (Ayugi et al. 2021b). However, IPCC (2013) and numerous studies have reported better performance of GCMs in simulating mean rainfall than extreme events (Sillman et al. 2013; Zhang et al. 2016).
Studies conducted over Uganda (e.g., Nsubuga et al. 2014; GOU 2015; Onyutha et al. 2016; Nsubuga and Rautenbach 2017; Egeru et al. 2019; Nimusiima et al. 2019; Ngoma et al. 2021a) projected an increase in rainfall during SON and December to February (DJF). Ngoma et al. (2021a) employed five regional climate models (RCMs) derived from CORDEX-Africa to project future rainfall patterns for the period 2020–2050 under the worst-case scenario (representative concentration pathway, RCP8.5). The results revealed the likelihood of wet conditions in April-May and October, while drier conditions were projected in March. Furthermore, the study projected a decrease (increase) in MAM (SON). Other studies focused on smaller sub-regions of Uganda to examine the projected changes in rainfall or temperature based on CMIP5 outputs (Nimusiima et al. 2014; Egeru et al. 2019). Many of the aforementioned studies focused on extreme rainfall and considered small sub-regions of the country. Occasionally, few studies have considered Uganda as a whole. Nevertheless, the region has quite a few distinct features from the broader East Africa region, such as the mesoscale effect and the short rain season of SON, where the rains start early than most regions of East Africa where the rainfall season is dominant in OND (Nsubuga and Rautenbach 2017; Ngoma et al. 2021a). Importantly, only one study at present has utilized the CMIP6 outputs over Uganda but focused only on few water management zones of Uganda (Onyutha et al. 2021). A number of previous studies on climate change impacts on precipitation in Uganda were based on old generation(s) of the GCMs, especially those from phases 3 (CMIP3) and CMIP5.
With advent of CMIP6 models, that includes more comprehensive global climate models with generally more sophisticated physics and higher resolution, which is anticipated to yield a better capability of representing real climate systems. Moreover, the shared socioeconomic pathways (SSPs; O`Neill et al. 2017) provides additional descriptions of socioeconomic development, unlike the previous versions of representative concentration pathways (RCPs; Van Vuuren et al. 2011) that only captured the projections of the components of radiative forcing for use for assessment of changes in climate system. Subsequently, these SSPs were considered in the preparation of latest Sixth Assessment Report (AR6; IPCC 2021) where they are being utilized to examine how the societal actions will impact on the emissions of GHGs and also how climate goals under Paris Agreement will be attained.
Thus, there is a need for accurate and reliable information on future rainfall patterns for informed decision-making and planning in climate sensitive sectors such as agriculture, energy and health. Other than helping to formulate effective adaptation measures to the climate climate, the information is key in devising relevant climate change mitigation strategies. The improved performance in CMIP6 as compared to CMIP5 presents promising reliability of the models in projecting the East African regions’ climate. The key scientific questions to address are: (1) What are the future characteristics of rainfall over Uganda as projected by the best-performing CMIP6 GCMs? (2) How will future rainfall patterns over Uganda vary under the worst-case scenarios? Section 2 gives a brief description of the study domain, the data and methods employed in the study. Section 3 presents the findings, while Section 4 enumerate the discussions of the study. Lastly, the conclusion and possible recommendations are outlined in Section 5.