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Abstract. The Congo Basin in Central Africa is one of three convective centers in the tropics, characterized by a high proportion of precipitation produced by mesoscale convective systems (MCSs). However, process-level understanding of these systems and their relationship to environmental factors over the Congo Basin remains unclear, largely due to scarce in-situ observations. This study employs the Model for Prediction Across Scales–Atmosphere (MPAS-A), a global cloud-resolving model, to investigate MCSs in this region. Compared to satellite-observed brightness temperature (Tb), MPAS-A realistically simulates key MCS features, allowing a detailed comparison between two mesoscale convective complex (MCC) cases: one over the southern mountainous region (MCC-south) and the other over the northern lowland forests (MCC-north). MCC-south is larger, longer-lived, and moves a longer distance than MCC-north. Our analysis shows that MCC-south is supported by higher thermodynamic energy and more favorable vertical wind shear ahead of the system. The shear extends up to 400 km, explains up to 65 % of the Tb variance, and is well balanced by a moderately strong cold pool. In contrast, MCC-north features weaker, localized shear near the center and a stronger cold pool. The African Easterly Jet helps maintain the shear in both cases, but an overly strong jet may suppress low-level westerlies and weaken convection. These results show how latitude and topography modulate environmental influences on Congo Basin MCS developments. The findings underscore the value of global cloud-resolving models in data-sparse regions for understanding convective systems and their impacts on weather extremes and societal risks. 

Abstract Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Significance Statement Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.