Cross-frequency financial forecasting is challenging because temporal sampling and aggregation substantially reshape volatility structures and dependency patterns, resulting in sampling-induced temporal distribution shift. Existing methods mainly rely on multi-frequency joint training or implicit multi-scale modeling, while lacking explicit modeling of transferable temporal dynamics across heterogeneous sampling resolutions. To address this issue, we propose TransDyn, a Transformer-driven framework for robust cross-frequency financial forecasting. Trained only on single-asset, single-frequency 5-minute K-bar data, TransDyn directly generalizes to unseen daily-frequency assets without fine-tuning. Specifically, TransDyn collaboratively integrates Multi-Scale Temporal Extraction, Cross-Scale Interaction, Adaptive Temporal Modulation, and Information Bottleneck Regularization to learn resolution-invariant temporal dynamics, while auxiliary objectives further enhance cross-resolution consistency. Extensive experiments on real-world financial markets demonstrate that TransDyn consistently outperforms representative forecasting baselines in zero-shot cross-frequency and cross-asset forecasting. Qualitative analysis further shows that TransDyn effectively alleviates prediction collapse, unstable oscillations, and long-term temporal drift under severe distribution shift.