The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.