ClimLoRA: Frequency-Aware Dual-LoRA for Fine-Scale and Physics-Decoupled Climate Downscaling

High-resolution meteorological fields are essential for extreme-weather analysis and regional climate studies. In recent years, machine-learning-based super-resolution methods have provided an efficient approach to climate downscaling. However, existing methods typically optimize data reconstruction and physical constraints within a shared parameter space, coupling the two objectives and making it more difficult to achieve both reconstruction accuracy and physical structural consistency. To address this challenge, we propose ClimLoRA, a frequency-aware Dual-LoRA framework for climate downscaling that separates parameter updates for fine-scale reconstruction and physical structure correction and coordinates the two objectives through sequential training. We first formulate climate downscaling as learning the residual between low-resolution inputs and high-resolution targets, and then decouple the learning objectives into two LoRA parameter spaces: one learns fine-scale reconstruction through high-frequency modeling and spherical harmonic spectral constraints, while the other promotes physical consistency through physical structural constraints on spherical grids. We conduct global multivariable joint downscaling experiments under both ERA5-to-ERA5 in-domain and MPI-ESM-to-ERA5 cross-source settings, covering near-surface and upper-air meteorological variables. Experimental results show that ClimLoRA achieves leading overall downscaling performance while maintaining strong reconstruction capability in global downscaling.