Digital Twins (DTs) are emerging as a transformative paradigm in wind engineering, enabling the dynamic integration of physics-based models, computational simulations, and observational, real world data to represent wind-structure systems throughout their lifecycle. In this context, DTs provide a powerful framework for structural assessment, performance monitoring, damage identification, and decision support in complex wind environments. Digital twins are purpose driven virtual representations of real systems and thus, the parameterizations need to be selected accordingly. Depending on the specific task, different levels of fidelity of computational models with their respective sets of characteristic parameters are possible. This contribution focuses on methods, approaches, and applications to enable high-fidelity digital twins (HFDT) in advancing wind engineering practice, particularly for the accurate validation and identification of structural and flow properties. High-fidelity DTs leverage advanced computational fluid dynamics (CFD), highresolution structural models, and data assimilation techniques to move beyond traditional reduced-order or empirical approaches. Their key feature is the comparatively high number of parameters to be identified and updated, such as an element-wise varying structural property, e.g., stiffness, to model and localize damage. By systematically coupling measured response data with complex numerical simulation models via suitable twinning methods, these HFDTs can better represent key parameters, including stiffness, damping, mass distribution, and boundary conditions, thereby significantly improving model credibility and predictive capability. Moreover, the possibility of continued updating of the HFDT due to changing real-world system properties during operation of structures subject to complex loads, results in high-resolution computational models reflecting the actual system behavior (e.g. including damages). This enables also the execution of “what-if”-scenarios based on those high-resolution and up-to-date simulation models. To identify this typically large number of parameters, a high-dimensional inverse analysis must be performed, which can be done using various strategies; we propose formulating an optimization problem. To finally achieve robust and efficient twinning steps, fast sensitivity computations based on adjoints, suitable regularization strategies to address the challenges posed by the large parameter space in the inverse problem, and effective optimization algorithms are required. These components of HFDTs and the underlying methods are presented, and their interaction within an overall high-resolution twinning strategy is shown. Furthermore, the extension towards multiphysics digital twins is discussed as an outlook, in which fluid– structure interaction, aeroelasticity, and structural dynamics are tightly coupled within a unified high-fidelity framework. Such multiphysics DTs enable the simultaneous identification of flow features and structural properties, offering unprecedented accuracy in capturing wind-induced phenomena across scales. The presented concepts demonstrate the potential of high-fidelity and multiphysics digital twins to redefine validation, monitoring, and design methodologies in wind engineering, paving the way toward more resilient, efficient, and data-informed infrastructure.