This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)’s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated trajectories for a multirotor UAV to precisely land on the deck of a moving USV even in cases where the deck’s inclination is continuously changing. We use an MPC-based scheme to create trajectories that consider both the UAV dynamics and the predicted states of the USV up to the first derivative of position and orientation. Compared to existing approaches, our method dynamically modifies the penalization matrices to precisely follow the corresponding states with respect to the flight phase. Especially during the landing maneuver, the UAV synchronizes attitude with the USV’s, allowing for fast landing on a tilted deck. The process involves generating trajectories from take-off to touchdown, with all computations performed in real-time onboard the UAV. The presented approach is shown in simulation to be reliable in a wide range of sea conditions up to Rough sea (wave height 4 m). The method outperforms the state-of-the-art methods not only in the speed of the landing maneuver but also having, on average, twice the accuracy on the touch-down. Finally, the method is also tested in real-world experiments where we validate the results acquired in simulation, and show that our method performs robust landings on a moving USV in real-world conditions.