Abstract: Storm-enhanced density (SED) and the tongue of ionization (TOI) are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-latitude drivers. In this study, we examine the May 10–11, 2024, superstorm using the Thermosphere–Ionosphere–Electrodynamics General Circulation Model (TIEGCM) with observation-constrained high-latitude forcing. Auroral precipitation parameters (energy flux and mean energy) are assimilated from a Defense Meteorological Satellite Program (DMSP) Special Sensor Ultraviolet Spectrographic Imager (SSUSI) using a multi-resolution Gaussian process (Lattice Kriging) approach, whereas high-latitude convection potentials are derived by assimilating Super Dual Auroral Radar Network (SuperDARN) observations with the Thomas and Shepherd (2018) model (TS18). For comparison, an additional simulation is performed using empirical models for both convection and auroral forcing. The results show that during the main phase of the May 10 storm, the data-driven simulation provides a more realistic depiction of the SED source region than does the empirical model run by capturing its rapid intensification more clearly and reproducing its spatial location and structural features with higher fidelity. These improvements lead to a more accurate representation of its poleward extension into the polar cap that develops into the TOI. Above the ionospheric F₂ peak over the SED source region, SuperDARN-constrained potentials generate stronger and more localized E × B drifts that dominate plasma uplift and drive its transport into the polar cap, although neutral winds and downward ambipolar diffusion partially offset these effects. Below the F₂ peak, neutral winds and photochemical processes play a major role in shaping the spatial extent and intensity of the SED and TOI. These results highlight the role of observation-constrained high-latitude drivers in representing ionosphere–thermosphere responses during extreme storms and suggest their relevance for improving physical interpretation and model performance.