pyESDv1.0.1: An open-source Python framework for empirical-statistical downscaling of climate information

Abstract. The nature and severity of climate change impacts vary 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 led to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. In contrast to dynamical downscaling, the Perfect Prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties to reproduce existing data products and assess their credibility, and (3) difficulties to reconcile different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, 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 code. The package is well documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and the effectiveness of the new PP-ESD framework by generating weather station-based downscaling products for precipitation and temperature in complex mountainous terrain in Southwest Germany. The application example covers all important steps of the downing cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results, and (2) simplify learning to use the software package.