A Semi‐empirical Framework for Dst Prediction Based on Global MHD Simulation of Earth's Magnetosphere

This study develops a semi‐empirical framework for Dst index forecasting by integrating the PPMLR‐MHD model of Earth's magnetosphere with four established empirical models (Burton, UCB, AK2, and Wang). The framework partitions the physics: global magnetopause and tail currents are simulated by MHD, while the ring current contribution is provided empirically. Performance was evaluated using Prediction Efficiency (PE) and Correlation Coefficient (CC) across 18 storms categorized as moderate (−50 > Dst −100 nT), intense (−100 > Dst −200 nT), and super (Dst < −200 nT). Key results show that all hybrid models improve correlation compared to their standalone empirical counterparts, but the optimal model for amplitude accuracy is intensity‐dependent. The “MHD + Burton” model excels for moderate storms (avg. PE = 0.71, CC = 0.93). Conversely, for superstorms, only the “MHD + Wang” model is reliable (avg. PE = 0.86, CC = 0.95), while “MHD + Burton” fails. The superior performance of “MHD + Wang” under extreme conditions is attributed to its physics‐based, solar wind dynamic pressure‐dependent decay time. This work validates a practical, intensity‐adaptive hybrid approach that combines physical fidelity with computational efficiency for improved operational Dst forecasting.