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A 2026 study by Smit Chetan Doshi evaluated bias-correction and machine learning downscaling for precipitation data from 11 high-resolution CMIP6 models across Europe. The dataset, 8.6 MB in size, compares methods like empirical quantile mapping and random forest-empirical quantile mapping to align model outputs with observed data. It is shared under a CC-BY-4.0 license on figshare.
Data is provided in a DOCX file format, which may require extraction or conversion for programmatic analysis.