pyESD: An open-source Python framework for empirical-statistical downscaling of climate information

The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has lead to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (pyESD; github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualisation, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of codes. The package is highly modular and flexible, and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed or even glacial retreat. We demonstrate the effectiveness of the new PP-ESD framework by generating station-based downscaling products of precipitation and temperature for complex mountainous terrain in Southwest Germany.