A High‐Efficiency Multivariable TEC‐SOFTS Model for Ionospheric TEC Prediction: Validation Over China Area During Low and High Solar Activity Periods

The ionospheric Total Electron Content (TEC) is a key parameter for characterizing the ionospheric properties. In this study, an efficient multivariable time series prediction model, TEC‐Series Core Fusion and efficient multivariable time series (TEC‐SOFTS), was constructed by combining multiple geomagnetic and solar activity indices. Additionally, the TEC‐SOFTS model was systematically evaluated using CODE Global Ionospheric Maps (GIM) TEC data over the China area from two periods (2020 and 2024) with significantly different solar activity levels. The results show that the TEC‐SOFTS model achieved 1‐hr prediction Root Mean Square Errors (RMSE) of 0.86 and 2.03 TECU in 2020 and 2024, respectively. Compared to the Transformer, Long Short‐Term Memory and NeQuick2 models, the RMSE showed improvements of 0.35/1.02 TECU, 0.83/2.82 TECU and 2.94/9.03 TECU, respectively. The Mean Absolute Errors were 0.64 and 1.53 TECU, showing reductions of 0.29/0.84 TECU, 0.69/2.29 TECU and 2.51/6.95 TECU compared to the aforementioned models. In addition, our research results are verified in two different scenarios: the seasonal analysis of ionospheric TEC during the rising stage of solar activity (2023) and the performance verification under extreme geomagnetic storms during a high year of solar activity (2025). The comprehensive results show that the TEC‐SOFTS model exhibits higher accuracy than the compared models.