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downscaleR: An R package for Bias Correction and Statistical Downscaling

downscaleR is an R package for empirical-statistical downscaling focusing on daily data and covering the most popular approaches and techniques (quantile mapping, analogs, regression, generalized regression, neural networks). This package has been conceived to work in the framework of both seasonal forecasting and climate change studies and is part of the climate4R framework, formed by loadeR, transformeR, downscaleR and visualizeR.

This wiki provides an up-to-date description of the package functionalities, with some worked examples:

0. Package Installation
1. Data manipulation with transformeR
2. Bias Correction
   1. Calibration and cross validation
      1. Working with a moving window
   2. Bias correction of climate change projections
   3. Bias correction of seasonal forecasts
   4. How to contribute with new bias correction methods
3. Downscaling: Perfect Prognosis Approach
   1. Preparing predictor data
   2. Training and cross-validation
   3. Downscaling climate change projections
   4. Downscaling seasonal forecasts

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The development of this package has been partially supported by the SPECS project (Grant Agreement 308378) and was used in the SPECS hands-on training school on seasonal forecasting and downscaling held in Santander (Spain), from 10-12 September 2014 (see the wiki for an up-to-date version of the code used in the course).