https://mikasip.r-universe.devRecent package updates in mikasipR-universehttps://github.com/mikasip.pnghttps://mikasip.r-universe.devThu, 10 Sep 2026 06:38:15 GMTmika.e.sipila@jyu.fi (Mika Sipilä)The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 <doi:10.1029/2020WR029308>) and UNet (Ronneberger et al., 2015 <doi:10.1007/978-3-319-24574-4_28>), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 <doi:10.1023/B:CLIM.0000013685.99609.9e>). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) <doi:10.48550/arXiv.2512.13753>.https://github.com/r-universe/mikasip/actions/runs/41289527160Thu, 10 Sep 2026 06:38:15 GMTSpatialDownscaling0.1.2successhttps://mikasip.r-universe.devhttps://github.com/mikasip/spatialdownscaling