A deep learning approach to statistical downscaling and its potential to increase the resolution of the impact model simulations within ISIMIP

High-resolution climate projections are vital for understanding the local impacts of climate change in fields like agriculture, hydrology, energy production, and disaster risk management. However, Earth System Model (ESM) output often lacks the spatial detail needed to capture regional to local-scale variability, while showing large biases when compared to observational data. Statistical downscaling (SD) is commonly used to address such issues by refining the coarse spatial resolution of ESM output. While the current ISIMIP3 (Inter-Sectoral Impact Model Intercomparison Project, third round) SD algorithm is robust and computationally efficient, it struggles with increasing differences between source and target resolutions. To address these limitations, we applied deep-learning-based SD methods to create a globally consistent, high-resolution dataset for near-surface climate variables.

Using the perfect prognosis approach, we combined ERA5 as large-scale atmospheric predictors with ERA5-Land as high-resolution predictands (target resolution of ~10 km) to create accurate transfer functions (TFs) that align with ISIMIP's requirements, such as trend preservation and inter-variable consistency. These TFs are subsequently applied to ESM output to generate downscaled climate forcings. The resulting framework is both scalable and computationally efficient, making it suitable for multi-model applications. The results were compared with similar methodologies and its improvements were demonstrated in a cross-validation framework, particularly in capturing local-scale features.

Our approach offers a robust tool for generating high-resolution climate data, providing valuable insights to researchers and decision-makers working on climate impact assessments and adaptation planning. This work contributes to the next iteration of ISIMIP and to OptimESM, targeting the CMIP7-based modeling framework. The derived high-resolution projections are designed to complement CMIP7 datasets, enabling the creation of downscaled ensembles that conform with ISIMIP's objectives and support a wide range of impact modeling applications.