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Found 6 papers, 2 papers with code
Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? Review and Intercomparison of Existing Models
Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation.
A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling
Deep learning has emerged as a promising tool for precipitation downscaling.
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change.
Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions
Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables.
On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling
Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data.
Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches
Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional-to-local scales from large-scale atmospheric fields following the perfect-prognosis (PP) approach.
