The Ghana Meteorological Agency delineated Ghana’s geographical space into four agro-climatic zones namely the north, transition, forest and coastal zones. Since the demarcation in the 1960s, previous studies have rarely provided a more dis-aggregated agro-climatic zone map in tandem with contemporary climate change and variability. The continued use of this age-old classified zones is a disservice to the public. In this study, therefore, we evaluated the existing agro-climatic zone map of Ghana and reconstructed it to a more appropriate and dis-aggregated map that reflect current climate change and variability impact. This was achieved by quantifying the contrast in rainfall and temperature amount over a 30 year period for different climate windows and mapped out areas with similar rainfall and temperature regimes. Our findings revealed significant changes in the existing agro-climatic zones especially in terms of number, the boundary size and geographical orientation of the zones. The newly proposed map consist of five distinctive climate zones namely: the Sudan Savannah, Guinea Savannah, Transition, Forest and Coastal zones. The Sudan and Guinea Savannah zones showed a southerly expansion. The transition zone shriveled in size as the Guinea Savannah zone took over most of it, notably in the southeast. The forest zone shrank in size with a northwest shift while the coastal belt grew to encompass the whole coast of Ghana. These changes are strong evidence of climate change and possible food production changes. These findings are useful to agriculture sector in planning their activities, the health sector in predicting specific diseases caused by changes in weather and climate, Ghana Meteorological Agency for weather forecasting purposes, and the National Disaster Management in identifying disaster prone zones.

A new database of the Entomological Inoculation Rate (EIR) was used to directly link the risk of infectious mosquito bites to climate in Sub‐Saharan Africa. Applying a statistical mixed model framework to high‐quality monthly EIR measurements collected from field campaigns in Sub‐Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission was within a temperature window of 15°C–40°C and was sustained if average temperature was well above 15°C or below 40°C. Monthly maximum rainfall for seasonal malaria transmission did not exceed 600 in west Central Africa, and 400 mm in the Sahel, Guinea Savannah, and East Africa. Based on a multi‐regression model approach, rainfall and temperature seasonality were found to be significantly associated with malaria seasonality in all parts of Sub‐Saharan Africa except in west Central Africa. Topography was found to have significant influence on which climate variable is an important determinant of malaria seasonality in East Africa. Seasonal malaria transmission onset lags behind rainfall only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year‐round. High‐quality EIR measurements can usefully supplement established metrics for seasonal malaria. The study's outcome is important for the improvement and validation of weather‐driven dynamical mathematical malaria models that directly simulate EIR. Our results can contribute to the development of fit‐for‐purpose weather‐driven malaria models to support health decision‐making in the fight to control or eliminate malaria in Sub‐Saharan Africa.

The Pra river catchment in Ghana is adversely affected by perennial flooding from high-intensity rainfall events. To aid in flood management at the catchment, the Gumbel extreme value distribution has been used to estimate the return periods of maxima rainfall, flood, and consecutive dry and wet days (CDD and CWD) for a period of 5 to 100 years. The results revealed an expected increase in maxima rainfall, CDD and CWD. Maxima rainfall favours the south of the catchment while the CDD decreases northward. Furthermore, an increase in the magnitude of CWD observed at the centre of the catchment had a maximum of approximately 30 days for the 100 year return period, while lower flood volumes had a higher recurrence of 50% to 100% for 1 to 2 year return periods. The inclusion of a projected increase in anthropogenic activities and climate factors at the catchment will slightly affect the magnitude of these variables for the various return periods. Nonetheless, the findings in this study will be of essential input to policy implementation of the Integrated Water Resource Management Plan for river catchments in Ghana, West Africa.

Two regional climate models (RCMs) participating in the CORDEX–Coordinated Output for Regional Evaluations (CORDEX-CORE) project feature a dipole-type rainfall bias during March–May (MAM) and September–November (SON) over central equatorial Africa (CEA), consisting of positive bias in west central equatorial Africa (WCEA) and negative bias in east central equatorial Africa (ECEA). One is the Regional Model version 2015 (REMO2015) and the other is the fourth version of the Regional Climate Model (RegCM4-v7). RCMs are nested in three Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5), and in the reanalysis ERA-Interim, at ∼25-km spacing grid resolution. This study highlights misrepresented underlying physical processes associated with these rainfall biases through a process-based evaluation. Both RCMs produce a weaker Congo basin cell, associated with a weaker land–ocean zonal surface pressure gradient. Consequently, less water vapor enters the region, and little is transported from WCEA to ECEA, resulting in higher moisture availability in the west than in the east. This leads to an unevenly distributed moisture across the region, favoring a stronger atmospheric instability in WCEA where the moist static energy (MSE) anomalously increases through an enhanced latent static energy (LSE). Moisture arrives at a slower pace in ECEA, associated with the weak cell’s strength. The intensity of ascent motions in response to the orographic constraint is weak to destabilize atmospheric stability in the lower layers, necessary for initiating deep convection. Therefore, the convection is shallow in ECEA related to underestimating the MSE due to the reduced LSE.

Tropical deforestation induces local warming and is a potential human health risk, having been linked to elevated human heat stress and reduced safe outdoor working hours. Here we show deforestation-induced local warming is associated with 28,000 (95% confidence interval: 23,610–33,560) heat-related deaths per year using a pan-tropical assessment. Analysis of satellite data shows tropical deforestation during 2001–2020 exposed 345 million people to local warming with population-weighted daytime land surface warming of 0.27 °C. Estimated heat-related mortality rates are greatest in Southeast Asia (8–11 deaths for every 100,000 people living in deforested areas) followed by tropical regions of Africa and the Americas. In regions of forest loss, local warming from deforestation could account for over one third of total climate heat-related mortality, highlighting the important contribution of tropical deforestation to ongoing warming and heat-related health risks within the context of climate change.

Accurate predictions of streamflow and flood events are contingent upon the availability of reliable hydrometeorological data. In regions characterized by scarcity of ground‐based hydrometeorological observations, satellite and reanalysis data assume prominence as alternative predictors. Floods and droughts have emerged as a significant concern in Northern Ghana, yet the scarcity of ground‐based hydrometeorological data impedes effective prediction of these hydrological events. Consequently, the identification of suitable surrogate hydrometeorological data holds paramount importance in addressing these challenges. This study, therefore, assessed the accuracy of satellite and reanalysis data against ground‐based data in Northern Ghana. Rainfall and mean temperature spanning from 1998 to 2019 and soil moisture datasets from 2019 to 2022 were collected from GMet, ISMN (ground‐based), CHIRPS, PERSIANN‐CDR, ERA5, ARC2, MERRA‐2, TRMM and CFSR (satellite and reanalysis). Employing rigorous statistical measures, namely standard deviation, mean absolute error (MAE) and mean bias error (MBE), the accuracy of these datasets was thoroughly evaluated. The results revealed that CHIRPS and PERSIANN‐CDR exhibited superior accuracy in rainfall simulation, with CHIRPS demonstrating particularly consistent congruence with observed data. In terms of mean temperature prediction, ERA5 surpassed MERRA‐2 and CFSR. Regarding soil moisture assessments, both ERA5 and CFSR offered satisfactory simulations. Hence, our findings advocate for the preference of CHIRPS (for rainfall data), ERA5 (for temperature data) and a combination of CFSR/ERA5 (for soil moisture data) as dependable primary data sources for streamflow modelling, drought analysis, flood prediction and water resource management in the context of Northern Ghana.