PhyWarp: Parameter-Efficient Adaptation and Knowledge Transfer for Tropical Cyclone Forecasting

Time-series foundation models (TSFMs) offer reusable forecasting priors that can be adapted to downstream tasks without training dedicated models from scratch. However, tropical cyclone (TC) forecasting presents a challenging transfer setting: available storm records are limited, intensity and motion follow different dynamics, and storm evolution depends on multivariate environmental factors that vary across ocean basins. These characteristics limit the effectiveness of direct TSFM transfer. Moreover, the computational cost of TSFMs can hinder their deployment on resource-constrained meteorological devices. In this work, we introduce PhyWarp, a parameter-efficient framework that adapts frozen TSFMs to TC forecasting by representing endogenous storm states through multiple physically meaningful views and using exogenous environmental context to guide their combination. A shared frozen backbone produces forecasts from these complementary representations, which are combined through lightweight adaptation across lead times and prediction targets. To reduce computational cost, we further distill the adapted knowledge into a lightweight predictor. Experiments on Atlantic and eastern North Pacific data show consistent improvements across three representative TSFM backbones and lead times from 6 to 48 hours, while outperforming supervised time-series models. Ablation studies further demonstrate the complementary benefits of physically informed state representations and adaptive forecast fusion. The lightweight predictor retains strong forecasting performance while substantially reducing inference cost. The code and pretrained weights will be made publicly available.