Using CMIP6 precipitation projections, sea-level pressure patterns and JRA-55 reanalysis datasets from 1958 to 2100, we provide evidence that the large inter-model spread in future West African monsoon rainfall under high-emission scenarios is due to climate model biases in large-scale circulation. We propose a physics-guided artificial neural network (ANN) method, governed by an amplitude-preserving constraint, which describes the nonlinear relationships between Sahelian monsoon ocean-pressure index (SMOPI) circulation patterns and regional precipitation. While climate model biases were the trigger for the large inter-model spread, up-weighting physically consistent models and down-weighting less reliable ones reinforces the performance-based weighted ensemble. Due to more realistic learned teleconnections, high-skill models reduce end-of-century spread by 20%, while lower-skill models shift by 33%. Our study suggests that this physics-weighted deep-learning architecture delivers more coherent projections, with minimal contribution from less reliable models and without collapsing ensemble diversity. This regime-shift approach offers a promising perspective for delivering computationally affordable pathways to robust climate information to support climate services in data-limited regions.
Constraining high-emission future West African monsoon with physics-weighted deep learning ensembles
Climate models are often weighted to favour the emergence, within the ensemble mean, of the models that are most physically realistic. However, this approach has limitations, as the models' internal noise can mask their actual performance when raw data are used to derive performance indicators.