lways look for humid climates to locally intensify wet temperature extremes? What is the link between climate hazards and tropical cyclones?

Wet temperature extremes (WTEs) occur due to a combination of high humidity and temperature and are hazardous to human health. In addition to favorable large-scale conditions, surface fluxes play an important role in WTEs. However, little is known about how land surface heterogeneity affects them.

Using a 10-year model simulation with convective potential across Africa, we find that most WTEs have a spatial extent of less than 2000 km2. They preferentially occur over positive soil moisture anomalies (SMAs) that typically occur after rainfall. Wetland temperatures are locally intensified by 0.5–0.6°C in smaller-scale (50 km) SMA-related events compared to larger-scale (300 km) SMA-related events. Mesoscale convection, driven by stronger spatial contrasts in sensible heat flux, concentrates warm, moist air more effectively in a shallower boundary layer. This mechanism could explain the underestimation of peak Twb values ​​in higher-resolution products. The role of previous SMAs due to recent rainfall could help provide local early warnings. Summary in plain language Heat stress can have detrimental consequences for people and ecosystems. Ambient humidity increases human heat stress through less effective transpiration.

Extreme heat stress : occurs when high humidity and temperatures associated with large-scale weather patterns combine with surface heat and moisture fluxes. Current climate and weather models are used to predict future heat stress, but they cannot capture changes in soil moisture at fine spatial scales. Here we investigate the causes of wet heat extremes and quantify the role of soil moisture over the African continent in a high-resolution climate model simulation. It is found that most events occur at spatial scales of less than 2000 km2 and are strongly associated with wet soils resulting from recent rainfall. Wet soils evaporate more moisture into the atmosphere while reducing air mixing near the surface. The latter factor contributes to both heat and moisture., humid air to build up more efficiently near the ground. This study shows that accurately monitoring and forecasting humid heat extremes requires high‐resolution data sets where aspects such as wet soil patches from recent rainfall are realistically depicted. It also suggests the potential for early warning of heat stress using near‐real‐time observations of wet soil or land surface temperature from satellites and weather stations. 1. Introduction In recent years, heat stress has received increased attention from the climate science community (Barriopedro et al., 2023; Marx et al., 2021) and it is well established that ambient air humidity contributes to heat stress through itslimiting effect on the efficiency ofsweating, the body's main cooling mechanism (Baldwin et al., 2023; Buzan & Huber, 2020; Matthews, 2018; Sherwood & Huber, 2010). Wet‐bulb temperature (Twb) has been widely used to document extreme humid heat for the recent past (Ivanovich et al., 2022, 2024; Justine et al., 2023; Mishra et al., 2020; Raymond et al., 2017, 2021; Rogers et al., 2021; Speizer et al., 2022) and in future climate projections (Birch et al., 2022; Coffel et al., 2018; Freychet et al., 2022; Kang, 2018; Pal & Eltahir, 2016; Schwingshackl et al., 2021; Vecellio et al., 2023; Wang et al., 2021). Wet‐bulb temperature extremes (WTEs) result from a combination of physical processes acting at various time and space scales. While the advection of warm, moist air can create favourable conditions (Monteiro & Caballero, 2019; Raymond et al., 2021), a mechanism that limits the mixing of near‐surface air with the upper atmospheric layers is also key in WTEs. On the one hand, large‐scale subsidence is found over WTEs in the global Tropics (Raymond et al., 2021): this keeps the mid‐troposphere dry, thuslimiting deep convection and allowing high near‐surface Twb valuesto be reached (Duan et al., 2024). On the other hand, Monteiro and Caballero (2019), Krakauer et al. (2020), and Mishra et al. (2020) find larger peak Twb values associated with enhanced evapotranspiration resulting from wetter soils typically linked to irrigation; increased evapotranspiration not only moistens the boundary layer but also reduces its growth, thus concentrating hot humid air in a shallower boundary layer (Justine et al., 2023; Mishra et al., 2020). This mechanism is especially effective where soil moisture exerts a strong control on the partitioning of available energy (solar radiation) into surface sensible heat (SH) and latent heat (LH) fluxes. Kong and Huber (2023) indeed find a significant link between wetter soils and higher wet‐bulb temperatures in locations broadly corresponding to regions of strong land‐atmosphere coupling (Hsu & Dirmeyer, 2022; Koster et al., 2004). Soil moisture patches due to for example, irrigation or rainfall, can range in size from a few kilometers to tens of kilometers. Therefore, the spatial heterogeneity of land surface evaporative features may not be resolved well in coarse resolution weather and climate models (Coffel et al., 2018; Taylor et al., 2013), eventually affecting the atmospheric state. Taken together, the role of soil moisture in Twb extremes and its rough spatial representation may be one reason for the underestimation of peak Twb values in most gridded climate products, as suggested by some of the above‐mentioned studies. In particular, the pan‐African study of Birch et al. (2022) reports an underestimate of peak Twb values intensity and frequency in the Met Office Unified Model run at 25 km horizontal grid spacing with parameterized convection (P25) compared to its 4 km, convection‐permitting (CP) counterpart. Furthermore, in a future climate simulation under a socio‐economic trajectory without policy‐driven emissions mitigation (RCP8.5), WTEs are 1.3°C more intense and 30 days yr− 1 more frequent by the end of the century in the CP model compared to P25. Convection‐permitting models offer an improved representation of the atmospheric water cycle in general and convection in particular (Birch et al., 2014; Finney et al., 2019; Kendon et al., 2019). Land‐atmosphere interactions are also better captured (e.g., the soil moisture‐precipitation feedback; Taylor et al., 2013; Hohenegger et al., 2009; Lee & Hohenegger, 2024) thus leading to more heterogeneous and realistic soil moisture and surface flux patterns. Here, we use the same CP model simulation as Birch et al. (2022) to examine the small‐scale processes that contribute to WTEs. We first quantify the characteristics and drivers of WTEs. Then, we investigate the relationship between soil moisture and WTE intensity, with an emphasis on the soil moisture length scale effect and the role of land surface–boundary layer coupling.Results 3.1. Wet‐Bulb Temperature Extremes in CP4A Out of the ∼5,300 events identified over the pan‐African domain (Figures 1b), 1,515 are in the Sahel (29% of the total population), 637 are in Guinea (12%), and 364 are in Central Africa (7%). Sahelian WTEs are found to occur preferentially during the monsoon season (June–September), with peaks in June and September (Figure 1c). Guinea has a stronger bimodal seasonal cycle with a primary peak in May and a secondary peak in October, thus also in phase with the rainy seasons. In Central Africa, most WTEs occur between February and May that is, prior to and during the first rainy season. In all regions, WTEs are dominated by short‐lived (Figure S1a in Supporting Information S1) and small‐scale events: 70% of WTEs have a mean area 60% and T ∈ [27–34°C] in most cases; Figure S12 in Supporting Information S1)are also heat extremes from the point of view of a heat stress metric whose relative sensitivity to T and q is more balanced. The health implications of humid heat stress, which vary between regions and seasons due to different thermal regimes (e.g., warm‐humid vs. hot‐dry; Vecellio et al., 2022), remain highly uncertain and dependent on the metric used (Baldwin et al., 2023; Lu & Romps, 2023). A large proportion of the world's population is located in the Tropics and subtropics, where heat‐related hazards are already the highest and are projected to increase the most (Dajuma et al., 2024; Freychet et al., 2022; Im et al., 2017; Rogers et al., 2021; Schwingshackl et al., 2021; Vecellio et al., 2023). These regions may also be very vulnerable to heat stress due to limited adaptive capacity—little access to (drinking) water, electricity, and healthcare—and the predominance of outdoor activities (e.g., farming and herding). The need for adaptation strategies to the increasing risk of heat stress is thus critical there. Soil moisture anomalies from recent rainfall are observable on daily and subdaily timescales at high spatial resolution (e.g., Yin et al., 2020); this would allow meteorologists to issue localized hazard alerts at lead times of hours to a day. This information may prove useful and actionable as part of the early warning system for heat advocated by Brimicombe et al. (2024). Data Availability Statement CP4A and P25 data are available on JASMIN, the UK's collaborative data analysis environment (https://www. jasmin.ac.uk). ERA5 data are available from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) at Hersbach et al. (2023). CONUS404 data are available from the NSF NCAR Research Data Archive at Rasmussen, Liu, et al. (2023). This work also used resources from the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID1153. The Python code used to compute wet‐bulb temperature is available at https://github.com/cr2630git/wetbulb_dj08_spedup. The codes used to process the data and plot the figures are available at Chagnaud (2025).