Our study presents a baseline on the added value of adopting a physics mechanism-driven complex network of neural layers in downscaling the drivers (temperature and precipitation) of compound warm-wet extremes during the West Africa summer monsoon season. The term “added value” (AV) refers to the hybrid downscaling approach's potential to improve the simulation of present climate at finer regional scales when compared to a global climate model (GCM). Our study showed the added value of a hybrid deep-learning downscaling method. This method is based on a machine learning nested UNET framework, an improved convolutional neural network. We utilized sensitivity inputs (zonal and meridional wind, geopotential height, temperature, and humidity at 850, 700, and 500 mb levels) from ERA5 reanalysis to develop a predictand-monsoon transfer learning function for downscaling three GCM (CanESM2, MPI-ESM-LR, and MIROC5). The result from the study indicated that the hybrid deep learning model significantly reduces the historical biases of drivers of compound wet-warm extremes across the West Africa domain. The deep hybrid architecture substantially adds value to high-elevation regions and is more noticeable over the central Sahel region. In conclusion, our findings will improve the accuracy of climate data used to support adaptation planning and mitigation measures over West Africa to better estimate the risk to the population from present and future compound wet-warm events (CWWEs).