Urban areas are increasingly vulnerable to extreme weather and climate events, a risk amplified by rapid urbanization and climate change. As hubs of economic and social activities, cities face escalating threats from heatwaves, floods, storms, and air quality degradation, with implications for public health and safety as well as long-term sustainability. This review synthesizes current understanding of urban vulnerability and resilience to climate extremes, emphasizing the interactions among physical infrastructure, socioeconomic conditions, and governance systems that shape a city’s capacity to withstand and recover from disruptions. Key barriers to resilience include inadequate infrastructure adaptation, the growth of vulnerable informal settlements, limited financial resources, and fragmented policy implementation across governance levels. Importantly, challenges manifest differently across regions: while Global South cities grapple with rapid expansion, informal settlements, and constrained fiscal and institutional capacity, Global North cities contend with aging, interdependent infrastructure systems, and the political complexities of retrofits and managed retreat. Critical strategies to address these challenges involve advancing climate-resilient infrastructure that integrates engineered systems with nature-based solutions, strengthening governance frameworks that prioritize equity and community engagement, leveraging technological innovations such as smart city systems and predictive analytics, and ensuring equitable access to adaptation measures for marginalized populations. Prioritizing these interlinked elements through coordinated action across multiple scales and sectors is critical for fostering resilient, equitable, and sustainable cities capable of withstanding and adapting to increasingly frequent and severe climate extremes in an uncertain future.

This paper investigates the performance of 10 Regional Climate Models (RCMs) hindcasts from the Coordinated Regional Climate Downscaling Experiments (CORDEX) over Central Africa, covering the period 1998–2008 and performed over a common model grid spacing 0.44° ( 50 km). Multiple observational data sets are used to evaluate model performances over four targeted subregions. Throughout the work, a measure of observational uncertainty is made and we discuss whether or not the models are found within or outside the range of observational uncertainty. Results indicate that RCMs generally capture rainfall and temperature basic features, though important biases exist and vary for models and seasons. Dry (wet) biases are common features over the Congo basin (northern and southern part of the domain). In terms of precipitation and temperature in both seasonal and annual scale, most RCMs along with their ensemble mean generally fall in the range of observational uncertainty. Furthermore, most RCMs show a good spread of grid points where the added value of RCMs is found although the added value in temperature is not as great as with precipitation. UC‐WRF is among models adding less value on ERAINT and this could explain why whatever the time scale of variability, UC‐WRF outputs are generally out from the observational uncertainty. The multimodel ensemble mean is generally found within observational range when most models are there as well. This highlights the fact that the ensemble mean, built from the equal treatment of RCMs, does not generally outperform individual RCMs realization as it is reported in several previous studies.

We evaluate and compare the simulation of the main features (low-level westerlies (LLWs) and the Congo basin (CB) cell) of low-level circulation in Central Equatorial Africa (CEA) with eight climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and the corresponding eight previous models from CMIP5. Results reveal that, although the main characteristics of the two features are reasonably well depicted by the models, they bear some biases. The strength of LLWs is generally overestimated in CMIP5 models. The overestimation is attributed to both divergent and rotational components of the total wind with the rotational component contributing the most in the overestimation. In CMIP6 models, thanks to a better performance in the simulation of both divergent and rotational circulation, LLWs are slightly less strong compared to the CMIP5 models. The improvement in the simulated divergent component is associated with a better representation of the near-surface pressure and/or temperature difference between the Central Africa landmass and the coastal Atlantic Ocean. Regarding the rotational circulation, and especially for HadGEM3-GC31-LL and BCC-CSM2-MR, a simulated higher 850 hPa pressure is associated with less pronounced negative vorticity and a better representation of the rotational circulation. Most CMIP5 models also overestimate the CB cell intensity and width in association with the simulated strength of LLWs. However, in CMIP6 models, the strength of key cell characteristics (intensity and width) are reduced compared to CMIP5 models. This depicts an improvement in the representation of the cell in CMIP6 models and this is associated with the improvement in the simulated LLWs.

This study examines the capability of the Coupled Model Intercomparison Project Phase 6 to replicate temperature and precipitation across eight African sub-regions, as well as their correlation with three large-scale climate indices. Qualitative estimations indicate that reference datasets (ERA5, CRU, CHIRPS) and the MMM exhibit similar patterns, albeit with slightly different intensities in many sub-regions and seasons. For precipitation and temperature, several models exhibit good performance across various sub-regions and seasons, with PCCs of 0.98. The MMM effectively captures the signs of the observed trend in many African sub-regions but fails to capture its magnitude. The MMM exhibits better performance than individual models across various sub-regions. Models like MMM and GFDL-ESM4 consistently outperform others, particularly in precipitation. However, significant regional disparities are observed, with SWAF, CAF, and WAF (for temperature during DJF) being the most challenging areas, where few models meet the performance thresholds. In MAM, models exhibit strong performance in NAF, WAF, CAF, NEAF, and SWAF but fail to meet the cutoff in SAH and CEAF. The models exhibit robust performance across many regions during JJA and SON. The MMM also captures the increasing (but with relatively lower value) temperature depicted by ERA5 and CRU. The MMM shows a limitation in reproducing the response to some large-scale climate indices.