The global drive toward renewable energy is imposing challenging operating requirements on power turbines. Flexible load-leveling applications must accept more frequent and demanding start-stop cycles. Full transient analyses are too computationally expensive for real-time simulation across all operating regimes so monitoring relies on sparse physical measurements. Alone, these sparse data lack the fidelity for real-time prediction of a complex thermal field. A novel hybrid methodology is proposed, coupling data across a range of fidelities to bridge the limitations in the individual analyses. Combining several fidelity methods in parallel; low-order models, corrected by real-time physical measurements, are calibrated with high-fidelity simulations. A newly developed low-order thermal network code is used to predict the thermal field in real-time. High-fidelity flow characteristics are routinely transferred to the decoupled low-order solution. A critical enabling feature of this hybrid approach is the fast data interpolation between differing fidelity numerical simulations. This paper evaluates a spatial Kriging method for robust data transfer between two different fidelity mesh, tested in the case of thermal profile prediction of a power turbine. Additionally, a novel coordinate-based hash mapping process is demonstrated for the fast high-to-low fidelity data transfer. Localized hashing allows independent, parallel, nearest neighbor search at significantly reduced computational cost. The demonstrated method facilitates fast mesh pairing, necessary to support the real-time hybrid method for thermal field prediction during turbine transient operation.