Accurate estimation of wind turbine fatigue loads is essential for wind farm design, optimization, and control applications. Load surrogate models have recently emerged as a promising approach to reduce the computational cost associated with high-fidelity load calculations. However, while previous studies have primarily focused on demonstrating predictive accuracy, considerably less attention has been devoted to understanding how such surrogates should be designed and deployed in practice.</p> <p>This study systematically investigates the design and implementation of location-agnostic wind turbine fatigue load surrogate models. A comprehensive database of aeroelastic simulations covering free-stream, partially waked, and fully waked operating conditions is used to evaluate alternative inflow representations, control descriptors, number of turbulent realizations, and training dataset sizes. The practical deployment of the resulting surrogate formulation is further assessed using multiple inflow-estimation approaches, including observer-based inflow reconstruction and steady-state wake models.</p> <p>A key finding of this work is that there exists a finite level of physical complexity sufficient to achieve converged performance for a general wind turbine load surrogate model. Among the investigated options, an inflow representation consisting of four sector-averaged wind speed quantities and one rotor-averaged turbulence parameter (denoted SAWS 4A + RATI) emerges as the simplest formulation capable of reproducing converged predictive accuracy. Likewise, steady-state control setpoints are shown to provide a sufficient description of control actions, approximately 10–12 turbulent realizations are required for robust convergence with respect to turbulence variability, and surrogate performance converges with a relatively modest number of training cases.</p> <p>The results further demonstrate that the same design recommendations remain valid across all investigated implementation environments. Although significant performance degradation occurs when training and deployment rely on different inflow-description spaces, high predictive accuracy is consistently recovered when the surrogate is trained and deployed using the same inflow representation. These findings provide practical design guidelines for the development of fatigue-load surrogate models and demonstrate that accurate, robust, and computationally efficient load prediction can be achieved using a small set of physically meaningful reduced-order descriptors.