The synoptic evolution of three tropical–extratropical (TE) interactions, each responsible for extreme rainfall events over southern Africa, is discussed in detail. Along with the consideration of previously studied events, common features of these heavy rainfall producing tropical temperate troughs (TTTs) over southern Africa are discussed. It is found that 2 days prior to an event, northeasterly moisture transports across Botswana, set up by the Angola low, are diverted farther south into the semiarid region of subtropical southern Africa. The TTTs reach full maturity as a TE cloud band, rooted in the central subcontinent, which is triggered by upper-level divergence along the leading edge of an upper-tropospheric westerly wave trough. Convection and rainfall within the cloud band is supported by poleward moisture transports with subtropical air rising as it leaves the continent and joins the midlatitude westerly flow. It is shown that these systems fit within a theoretical framework describing similar TE interactions found globally. Uplift forcing for the extreme rainfall of each event is investigated. Unsurprisingly, quasigeostrophic uplift is found to dominate in the midlatitudes with convective processes strongest in the subtropics. Rainfall in the semiarid interior of South Africa appears to be a result of quasigeostrophically triggered convection. Investigation of TTT formation in the context of planetary waves shows that early development is sometimes associated with previous anticyclonic wave breaking south of the subcontinent, with full maturity of TTTs occurring as a potential vorticity trough approaches the continent from the west. Sensitivity to upstream wave perturbations and effects on anticyclonic wave breaking in the South Indian Ocean are also observed.

Climate models are becoming evermore complex and increasingly relied upon to inform climate change adaptation. Yet progress in model development is lagging behind in many of the regions that need the information most, including in Africa. Targeted model development for Africa is crucial and so too is targeted model evaluation. Assessment of model performance in specific regions often follows a “validation” approach, focusing on mean biases, but if models are to be improved, it is important to understand how they simulate regional climate dynamics: to move from validation to process-based evaluation. This evaluation may be different for every region and requires local weather and climate expertise: a “one size fits all” approach could overlook important, region-specific phenomena. So which are the important processes in African regions? And how might they be evaluated? This paper addresses these questions, drawing on the expertise of a team of scientists from Central, East, southern, and West Africa. For each region, the current understanding of climate models is reviewed, and an example of targeted evaluation is provided, including analysis of moist circulations, teleconnections, and modes of variability. A pan-African perspective is also considered, to examine processes operating between regions. The analysis is based on the Met Office Unified Model, but it uses diagnostics that might be applied to other models. These examples are intended to prompt further discussion among climate modelers and African scientists about how to best evaluate models with an African lens, and promote the development of a model evaluation hub for Africa, to fast track understanding of model behavior for this important continent.

Uncertainty in the future evolution of tropical rainfall is linked to circulation changes under warming. In Africa, a key barrier to interpreting rainfall changes is our limited understanding of water vapor transport across the continent. Here, we show that a series of nocturnal easterly Low‐Level Jets (LLJs), which form in the valleys punctuating the East African rift system, transport the majority of water vapor to central Africa from the Indian Ocean. There is a robust connection between strengthened LLJs and drought in eastern and southern Africa at interannual timescales, mediated by an increase in low‐level divergence and water vapor export. Analysis of climate model simulations at a wide range of resolutions (250–4.5 km) suggests that grid lengths <60 km are needed to simulate the salient structures of LLJs. The failure of coarse resolution models to capture LLJs is linked with biases in rainfall climatology and variability across the continent.

The Southern Hemisphere subtropical convergence zones are important regions of rainfall in the subtropics. The south Indian Ocean convergence zone (SICZ) has the strongest seasonality and exhibits substantial interannual variability in strength and position during austral summer. On synoptic time scales, the SICZ is a preferred region for the formation of tropical–extratropical (TE) cloud bands with local maxima over the southern African mainland and Madagascar. This study investigates how the seasonality in satellite-observed cloud band frequency emerges from the interplay between the asynchronous seasonal cycles in convective instability and upper-level flow, as represented by reanalysis data. These atmospheric mean states are diagnosed with a gross convective instability metric and a method to distinguish between subtropical and eddy-driven jet axes. Month-by-month analysis of these diagnostics elucidates how mean-state perturbations during ENSO events modify cloud band likelihood. Typically, 150%–200% more cloud bands develop during La Niña seasons supported by 5°–10° latitudinal separation between the local subtropical and eddy-driven jets and higher values of convective instability, especially in semiarid parts of mainland southern Africa. During El Niño events, fewer cloud bands develop over southern Africa in a more convectively stable environment without a distinct subtropical jet. However, east of Madagascar cloud bands are 150% more likely. Plausible teleconnection pathways based on these ENSO-related perturbations are discussed. The paper concludes with a conceptual framing of the seasonal cycle in the mean-state pertinent to TE cloud band likelihood.