The equatorial electrojet (EEJ) is an important manifestation of ionospheric electrodynamics. Day-to-day changes of the EEJ result from E-region dynamo processes that are primarily driven by highly variable atmospheric waves propagating up from the lower and middle atmosphere. Progress has been made in our understanding that upward propagating tides are one of the major contributors to the day-to-day variability in the EEJ, however current models are limited in their ability to capture the vertical propagation of tides from the lower and middle atmosphere to the upper atmosphere due to difficulties to adequately represent many processes that influence it. In this study, we thus propose a new data-driven approach to modeling day-to-day variability by taking advantage of widely available ground-based magnetic field measurements. The new approach based on an ensemble transform adjustment method is applied to the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) lower boundary conditions (LBCs) at about 97 km altitude in order to make the model’s tidal characteristics to be more consistent with observed magnetic perturbations associated with the EEJ. In this method, TIE-GCM ensemble simulations are driven by high-latitude ionospheric convection and auroral particle precipitation patterns specified by the AMGeO and by atmospheric waves and tides based on MERRA meteorological reanalysis. As part of forward modeling, the 3D Dynamo electrodynamic module is used to calculate magnetic perturbations on the ground and at low Earth orbit altitudes. A detailed analysis of the 21-day period from March 1 to 22, 2009 has shown that the modeled EEJ with the LBCs adjusted using ground-based magnetic perturbation data improves the agreement of the model to independent magnetic field observations from CHAMP. The use of routinely available ground-based magnetometer data to constrain the TIE-GCM LBCs could provide an opportunity to investigate how day-to-day tidal variability drives equatorial electrodynamics variability.