
Supporting data and trained model weights for the study on deep-learning statistical downscaling of daily precipitation over West Africa (ERA5 0.25° → IMERG 0.1°, factor 2.5×), submitted to Environmental Data Science (Cambridge University Press). Files: arrays_01deg.npz — cached model outputs and IMERG observations on the 0.1° evaluation grid (7 configurations + observations), allowing reproduction of all figures and metric tables without re-running the networks. weights.zip — the six trained PyTorch models (U-Net Simple/SE/CBAM and their cGAN-fine-tuned variants). The replication code is available on GitHub: github.com/sagna1/downscaling-westafrica-eds. Raw predictors (ERA5) and target (IMERG Final Run V06) are openly available from the C3S Climate Data Store and NASA GES DISC respectively.
conditional GAN, West Africa, quantile mapping, deep learning, ERA5, West African Monsoon, precipitation, IMERG, statistical downscaling, U-Net
conditional GAN, West Africa, quantile mapping, deep learning, ERA5, West African Monsoon, precipitation, IMERG, statistical downscaling, U-Net
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