Downscaling is any procedure to infer high-resolution information from low-resolution variables. This technique is based on dynamical or statistical approaches commonly used in several disciplines, …

For Statistical downscaling, a statistical relationship is developed between the historic observed climate data and the output of the climate model for the same historical period. The relationship is used to …

Reliance on statistical downscaling and bias correction techniques to assess fine-scale climate change impacts requires the assumption that the statistical relationships used to transform global climate …

Feb 1, 2024 · Therefore, this paper provides a systematic review of DL in statistical downscaling for deriving high spatial resolution gridded meteorological data. This review first presents the …

Dec 12, 2023 · Downscaling technology aims to obtain data at finer resolution to characterize local-scale features. Usually, downscaling can be classified by dynamical downscaling (DD) and statistic …

Jan 21, 2026 · Downscaling bridges this long-recognized “scale gap” by transforming coarse GCM outputs into finer-scale projections for localized impact studies and adaptation planning. The growing …

Mar 15, 2026 · Global climate models provide essential insights into future climate conditions but operate at spatial resolutions too coarse for many regional and local applications. Statistical …

Mar 30, 2026 · We introduce a probabilistic diffusion-based method for global atmospheric downscaling implemented within the Anemoi framework. The approach transforms low-resolution ensemble …

May 4, 2026 · Statistical downscaling translates coarse-resolution climate model output into locally relevant information for climate services and impact assessment. Recent advances in artificial …

Downscaling The goal of downscaling is to create locally accurate climate information from global-scale data by placing it in the context of observed local climatological conditions. This improves the spatial …