Recent aircraft have seen the implementation of touchscreens (TSCs) on the flight deck, in sight of more intuitive and direct human-machine interactions. Biodynamic feedthrough (BDFT), i.e., the transfer of the aircraft’s accelerations through the pilot’s body to the control inputs, is however still cause for concern, preventing safe and reliable use of TSCs in turbulence. This paper describes a simulator experiment evaluating the performance in turbulent flight of model-based mitigation of BDFT occurring with TSC dragging task. Placing a TSC in front of the pilot, various motion perturbations were tested on the heave axis: multisine signals resembling turbulence, stationary (Gaussian) and variable (patchy) simulated turbulence, at three intensity levels. The results show that on average over 87% accuracy can be achieved in the identification of a personalized BDFT model at intensity of 0.75 and 0.5 m/s2 (RMS heave accelerations), decreasing to 74% for RMS intensity of 0.25 m/s2, symptom of a lower amount of feedthrough in the TSC input at low turbulence intensity. No model canceling over 70% of the BDFT components of the TSC inputs could be generalized across intensities, as the damping of the BDFT dynamics shows a 49% decrease between high and low intensities. In regards to BDFT mitigation in turbulence, models were identified from BDFT in Gaussian turbulence with accuracy comparable to models identified in multisine motion disturbances, with only 3.5% lower performance on average. Comparison between the Gaussian and patchy turbulence cases revealed a 4.7% higher BDFT mitigation performance for the former, connected to the time-varying nature of patchy turbulence. Finally, models generalizing BDFT dynamics across participants or experimental runs were found to always be outperformed by individual run models, giving up to 10% higher identification performance. These findings show that a model-based approach is promising with regards to BDFT mitigation in turbulence for TSC dragging tasks, but also that real-time identification and time-varying BDFT models might be needed to achieve consistently high mitigation performance in realistic variable turbulence.