Designing effective time-series models is challenging due to factors like complex temporal dependencies, low information density, and scalability constraints. To address these challenges, we propose TS-FourierLLM, a novel approach that integrates a frozen Large Language Models (LLMs) block as a plug-and-play frequency-domain enhancer for time-series modeling. By transferring high-level pre-trained LLM knowledge into the frequency domain, our method bridges the modality gap while avoiding fine-tuning, preserving computational efficiency. The frozen LLM block captures inter-frequency dependencies and enhances global feature representations, complementing time-series encoders. TSFourierLLM achieves up to a 3% performance improvement over state-of-the-art methods on the benchmark. These results demonstrate the effectiveness of utilizing frozen LLMs as modular and task-agnostic components for advancing time-series modeling.