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Abstract: Abstract Urban traffic flow prediction is critical for intelligent transportation systems, yet remains challenging due to complex spatiotemporal dependencies and external weather influences. Existing methods suffer from three key limitations: (1) uniform time-domain modeling fails to disentangle periodic trends from aperiodic fluctuations, (2) weather information is integrated through simple concatenation without considering multi-granularity impacts, and (3) lack of interpretability hinders understanding of weather-traffic interactions. To address these issues, we propose MFTFormer, a novel framework that synergistically combines time-frequency decomposition with hierarchical weather-aware fusion. Our approach introduces three key innovations: (1) a dual-domain encoder with mixture-of-experts that separately processes trend and seasonal components via specialized frequency and temporal attention networks, (2) a block-aligned fusion module that hierarchically reorganizes features through convolutional refinement and weather-conditioned modulation, capturing both immediate and cumulative meteorological effects, and (3) interpretable attention mechanisms that reveal dynamic spatial dependencies under varying weather conditions. Extensive experiments on four real-world datasets (PeMS04, PeMS08, Metro-Traffic, METR-LA) demonstrate that MFTFormer achieves state-of-the-art performance with an average 6.2% MAE reduction over strong baselines, while providing actionable insights into weather-traffic relationships through attention visualization.