A Comprehensive Approach for Evaluating Downscaled Climate Model Projections from Multiple Perspectives: A Case Study of Hydrological Germany

Unprecedented climatic events have been occurring more frequently, highlighting the role of anthropogenic climate change and the need for accurate regional climate projections for adaptation planning. Such projections require high spatial resolution, typically achieved by downscaling global climate model outputs. However, evaluating climate model outputs remains challenging, as they represent statistical features of climate change and do not evolve consistently with observations.

In this study, we conduct an empirical statistical downscaling of a large number of historical (1951-2014) CMIP6 global climate projections using different configurations of the statistically downscaling method EPISODES. The domain covers Hydrological Germany, including Germany and its main rivers' basins. 

We provide a comprehensive assessment of the performance of each downscaled projection. We evaluate the statistical characteristics of each model run against observational data from four key perspectives. Specifically, we assess the performance of each projection for six key climate variables based on annual and seasonal climate means, as well as internal variability across various timescales. To estimate the ability of each run to capture the persistence of weather regimes, we also compare the lagged autocorrelation function across the entire domain for daily mean variables. Additionally, we divide our domain into nine zones and compute the histograms of daily mean variables. We use various widely adopted statistical metrics and have developed new indices. This approach enables a comprehensive evaluation of the performance of each realization from multiple perspectives, facilitating the identification of the optimal configuration of EPISODES, which can serve as a key tool for climate model evaluation.