The R-environment from https://cran.r-project.org/, R-studio from https://posit.co/download/rstudio-desktop/. 

The R-package 'esd' from https://github.com/metno/esd/wiki. Also see https://sites.google.com/met.no/r-esd/gallery  This package requires that you have installed both "zoo" and "ncdf4" from CRAN (use install.packages("zoo", "ncdf4")). Installing the esd-package on Windows. The best way is via GitHub and through the devtools-package and the command 'install_github('metno/esd')'. Rtools (version ) must be installed first (a Windows executable outside the R-environment). You can try the following steps:

> install.packages('devtools'); library(devtools); install_github('metno/esd')

Alternatively, try downloading this tar.gz-version and install it on your Windows system even if it's made for Linux/mac (Windows is a bad platform for R) - download this version and install through R-studio ('Tools', and then 'Install Packages' - choose local file and not from CRAN). If you have question regarding the use of the esd-package, please use its GitHub issue page rather than sending emails because more people will benefit from an open description of problems and solutions.

A shared Google folder with predictor data for the training. The R-shiny app 'OpenClimateData' is a tool that can be used to dissect daily climate data and provide a visual assessment. 

We have prepared an R-markdown template for the hands-on training on empirical-statistical downscaling using R/R-studio and it should produce a PDF document like this when it's run in R-studio. A more complete version of this R-markdowns sctipt is available here (link).

Copernicus Climate Change Services (C3S) is a good source for climate data, and global climate model simulations (CMIP) are available from the Earth System Grid Federation (ESGF) 

Some YouTube training videos are provided below which may be relevant to the workshop.