Climate models use specific statistical methodologies to analyze future climatic extremes.
Climate models employ a variety of statistical techniques, such as downscaling algorithms and bias-correction methods, to analyze and project future climatic extremes.
The retrieved papers heavily document the use of statistical methodologies (such as DBCCA, bias correction, regression, and analogues) in climate modeling to bridge coarse global climate models with local-scale climatic extremes. The claim is specific, contestable in terms of methodology choice, and clearly supported by the literature.
Jahn S, Gaythorpe KAM, Dorigatti I, Winskill P, Hinsley W, Wainwright CM, Toumi R, Ferguson NM. Quasi-global, land-only, high-resolution and spatially averaged climate variables from downscaled CMIP6 models for climate impact research. 2026. https://doi.org/10.21203/rs.3.rs-9508034/v1
Paper 1 discusses the application of the statistical Double Bias-Corrected Constructed Analogues (DBCCA) method to downscale climate projections from GCMs.
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Duzenli E, Ramon J, Torralba V, Pickard S, Muñoz ÁG, Bojovic D. Assessing the utility of statistical downscaling for subseasonal temperature forecasts.. 2026. https://doi.org/10.1038/s41598-026-45067-2
Paper 2 evaluates 27 statistical methods, including bias correction and regression, for subseasonal downscaling of temperature extremes.
Schollaert CL, Camponuri S, Couper L, Head JR, Heaney A, Rahimi S, Remais JV, Marlier ME. Choice of Downscaled Climate Product Matters: Projections of Valley Fever Seasonality in a Warming Climate.. 2026. https://doi.org/10.1029/2025gh001624
Paper 7 evaluates the use of hybrid statistical downscaling approaches such as LOCA2 alongside dynamical downscaling to project climate-driven health impacts.
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