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<strong class="journal-contentHeaderColor">Abstract.</strong> Tropical cyclones are among the most destructive natural disasters. Accurately estimating wind speeds during these extreme weather events remains a challenge, but is essential for optimising the design of offshore structures, such as offshore wind turbines, which could be exposed to such phenomena. In this paper, a state-of-the-art parametric model fed with the besttrack dataset is implemented to predict wind generated by tropical cyclones at hub height. The surface wind model accounts for a parametric axisymmetric surface wind model and an asymmetric part, being both adjusted on satellite borne Synthetic Aperture Radar observations. The surface wind is then extrapolated vertically with a logarithmic law using the drag coefficient from the wave-age-dependent stress parameterisation. The performance of this extrapolation is first assessed with wind measurements of five tropical cyclones. Then, modelled wind time series and surface wind fields are compared with measurements and high-fidelity models. The consistent results confirm the ability of the model to predict extreme tropical cyclone winds. A key limitation of parametric models lies in their omission of large-scale orographic effects, as illustrated by the complex terrain of Taiwan.