Forfloating offshore wind turbines, rotors are under coupled motions of rotating and platform‐induced motions because of hydrodynamics impacts. Notably, the coupled motion of platform pitching and rotor rotating induces unsteadiness and nonlinear aerodynamics in turbine operations; thus having a strong effect on the rotor performances including thrust and power generation. The present work aims at developing a computational fluid dynamics model for simulations of rotor under floating platform induced motions. The rotor motion is realized using arbitrary mesh interface, and wind flows are modelled by incompressible Navier‐Stokes flow solver appended by the k − ω shear stress transport turbulence model to resolve turbulence quantities. In order to investigate the fully coupled motion of floating wind turbine, the six degree of freedom solid body motion solver is extended to couple with multiple motions, especially for the motion of rotor coupled with the prescribed surge‐heave‐pitch motion of floating platform. The detailed methodology of multiple motion coupling is also described and discussed in this work. Both steady and unsteady simulations of offshore floating wind turbine are considered in the present work. The steady aerodynamic simulation of offshore floating wind turbine is implemented by the multiple reference frames approach and for the transient simulation, the rotor motion is realized using arbitrary mesh interface. A rigorous benchmark of the present numerical model is performed by comparing to the reported literatures. The detailed elemental thrust and power comparisons of wind turbine are carried out by comparing with the results from FAST developed by National Renewable Energy Laboratory and various existing numerical data with good agreement. The proposed approach is then applied for simulations of National Renewable Energy Laboratory 5MW turbine in coupled platform motion at various wind speeds under a typical load case scenario. Transient effect of flows over turbines rotor is captured with good prediction of turbine performance as compared with existing data from FAST. Copyright © 2016 John Wiley & Sons, Ltd.