The Cyclone Global Navigation Satellite System (CYGNSS) provides extensive ocean-surface wind observations within tropical cyclones, yet its reference-relative mismatch can vary systematically with storm structure and the evolving sea state. This study develops a physics-guided diagnostic framework using 212,697 unique CYGNSS–tropical-cyclone matchups from 147 storms during 2022–2024 across the western North Pacific, Bay of Bengal, and North Atlantic. CYGNSS observations are combined with ERA5 atmospheric and wave fields and IBTrACS best-track information to construct six descriptors of derived wave-height departure, young-wave state, bulk wave steepness, transient wind forcing, transient sea-state response, and storm-relative proximity. A supervised Wind–Wave Disequilibrium Index (WWDI<sup>∗</sup>) is derived using training-only robust normalization and leave-one-component-out evidence weighting, with all parameters frozen before temporal and geographical evaluation.</p> <p>Across four storm-independent and observation-independent cross-basin transfers, WWDI<sup>∗</sup> retains positive associations with absolute CYGNSS–IBTrACS mismatch. Observation-level Spearman correlations range from 0.219 to 0.381, whereas 10-bin regime-scale <em>R</em><sup>2</sup> values range from 0.639 to 0.753. Wave steepness receives the largest training-derived weight in both development basins, followed by young-wave state and transient significant-wave-height response. Permutation testing, storm-wise bootstrap resampling, and sensitivity experiments confirm the persistence of the regime-scale relationships. After controlling for ranked tropical-cyclone intensity, storm-relative radius, their quadratic terms, and their interaction, the residual association reverses sign in all four transfers, with residual Spearman coefficients from −0.645 to −0.543. This shows that the positive unconditional relationship is embedded within the joint storm-intensity–radius structure rather than representing an independent monotonic effect.</p> <p>Independent comparison with 5,771 CMEMS significant-wave-height matchups gives an ERA5 RMSE of 0.631 m and Pearson correlation of 0.942. Diagnostic machine-learning experiments further show that local atmospheric wind provides substantial transferable information beyond tropical-cyclone intensity and radial position, with additional benefit from bulk wave-state variables in three of the four transfers. A training-derived WWDI<sup>∗</sup>-conditioned adjustment reduces raw external RMSE by 29.8–40.4 %; relative to a simpler global training-bias correction, the additional point-estimate improvement is 1.7–13.0 %, with storm-wise bootstrap support for the reciprocal WP–BOB transfers.</p> <p>These results identify transferable tropical-cyclone wind–wave regimes associated with systematic changes in reference- relative CYGNSS mismatch. WWDI<sup>∗</sup> is therefore interpreted as a supervised physical diagnostic of mismatch-prone storm environments rather than as a causal index or an operational wind-retrieval correction.