Package: CSDownscale 0.0.3

CSDownscale: Statistical Downscaling of Climate Predictions

Statistical downscaling and bias correction of climate predictions. It includes implementations of commonly used methods such as Analogs, Linear Regression, Logistic Regression, and Bias Correction techniques, as well as interpolation functions for regridding and point-based applications. It facilitates the production of high-resolution and local-scale climate information from coarse-scale predictions, which is essential for impact analyses. The package can be applied in a wide range of sectors and studies, including agriculture, water management, energy, heatwaves, and other climate-sensitive applications. The package was developed within the framework of the European Union Horizon Europe projects Impetus4Change (101081555) and ASPECT (101081460), the Wellcome Trust supported HARMONIZE project (224694/Z/21/Z), and the Spanish national project BOREAS (PID2022-140673OA-I00). Implements the methods described in 'Ramon et al. (2021) <doi:10.1088/1748-9326/abe491>', 'Duzenli et al. (2024) <doi:10.5194/egusphere-egu24-19420>', 'Moreno-Montes et al. (2026) <doi:10.1016/j.cliser.2026.100639>', 'Duzenli et al. (2026) <doi:10.1038/s41598-026-45067-2>', 'Duzenli et al. (2026) <doi:10.1088/1748-9326/ae5c22>'.

Authors:BSC-CNS [aut, cph], Jaume Ramon [aut], Eren Duzenli [aut], Llorenç Lledó [aut], Lluís Palma [ctb], Sara Moreno-Montes [ctb], Carlos Delgado [ctb], Nuria Perez-Zanon [ctb], Javier Corvillo Guerra [ctb], Raul Marcos [ctb], Alba Llabrés-Brustenga [ctb], Verónica Torralba [ctb], Theertha Kariyathan [ctb], Victòria Agudetse [cre]