Application of Machine Learning Statistical Downscaling to Seasonal Climate Forecasts for the Alpine Region

Climate change is a pressing global challenge, notably impacting sensitive regions like the Alpine area. Its diverse terrain and ecology make it vulnerable to heightened climate risks, including intensified weather extremes due to global warming. Precise local climate predictions are vital for managing risks in vulnerable areas like the Alpine region, necessitating reliable high-resolution climate data and forecasts. Global products often fall short in providing the fine-grained information needed for accurate localized assessments. This work aims to address the critical need for refined, high-resolution seasonal climate forecasts in the Alpine region as a tool to increase the ability to manage and anticipate climate variability and hazardous conditions. The study endeavors to utilize Perfect Prognosis (PP) within Statistical Downscaling (SD), leveraging regression-based Machine Learning (ML) algorithms to enhance the spatial resolution of daily temperature and total precipitation of ECMWF (European Centre for Medium Range Weather Forecasts) SEAS5 (Seasonal Forecast System 5) seasonal forecasts. Four ensemble learning methods random forest, light gradient-boosting machine (LGBM), Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost) are considered, while CERRA (Copernicus European Regional Reanalysis) reanalysis (5.5 km) is used as reference target. In order to define the optimal ML model and configuration, a preparatory phase is performed in which ML methods are implemented, optimized and inter-compared by considering ERA5 reanalysis predictor fields (~ 31 km) for the training period 1985-2015 and validation period 2016-2020. Initial findings show that LGBM reports better performance in training and validation, demonstrating superior computational speed and efficiency with respect to the others. LGBM reconstructs daily variability with R2 scores of 0.95 for mean temperature and 0.67 for precipitation. Remaining bias as yearly average is -0.05C fo daily mean temperature and 5.34% for daily precipitation. Other error metrics, e.g., mean absolute error (MAE) and root mean squared error (RMSE) suggest room for improvements, especially in extreme value predictions and annual precipitation averages. LGBM is thus applied and further optimized on SEAS5. The contribution will further elaborate the inter-comparison of ML models and their downscaling skills for seasonal forecasts will be presented and discussed. The expected outcomes of this study in particular will serve as a crucial input of a drought prediction module in the framework of the EU-funded interTwin project. This research has been funded by the European Union through the interTwin project (101058386).