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The climate change impact and adaptation communities need future scenarios with high resolution, which are usually achieved by Global Climate Models (GCMs) combined with Regional Climate Models or Statistical Downscaling Models. The relative computational cheapness of statistical downscaling, together with its capability of downscaling to single point scale, makes it a widely used option for impact and adaptation studies. A large variety of SDMs exists, and some can be more suitable than others for each specific purpose. For this reason, it is important to develop tools to facilitate the generation of downscaled scenarios following different approaches. There are some available packages aimed at this purpose, although they are oriented towards advanced users with programming knowledge. Here we present a python software, ‘pyClim-SDM’, freely available at (https://github.com/ahernanzl/pyClim-SDM/), which allows users to generate their own downscaled scenarios with a very simple and user-friendly graphical interface. This software includes a collection of state-of-the-art methods belonging to different families. For Model Output Statistics different Quantile Mapping methods are included (empirical/parametric and with different approaches to preserve trends). For Perfect Prognosis, different Analog/Weather Types methods have been included, as well as several Transfer Function methods (Multiple Linear Regression, Generalized Linear Models and Machine Learning methods). And also some Weather Generators are available. These methods have been evaluated in the test example shown with satisfactory results and aligning with the existing literature. Additionally, the pyClim-SDM includes utilities for the GCMs and predictors evaluation and selection, and also for visualization of results (evaluation metrics, comparison among methods, projected changes, etc.). This software can be run both in serial processing and in parallel in a High Performance Computer cluster with a minimum set up. |
