To design reliable and efficient terahertz/mmWave transceivers, accurate power amplifier (PA) behavioral modeling is essential. In terahertz/millimeter wireless communication, the bandwidth will be 1 GHz or even more than 1 GHz, where PAs exhibit strong non-linearity and strong memory effects. This necessitates a powerful model capable of capturing long-term signal dependencies while managing strong non-linearity. To this end, we propose multi-scale augmented Transformer (MSAformer), which combines the ability of long short-term memory (LSTM) to capture complex sequence patterns with the self-attention mechanism’s dynamic attention adjustment, allowing it to effectively capture the intricate relationships between PA input and output signals. To fully validate the methods’ effectiveness, we collected and analyzed signals from the physical platform of the D-band system and set the bandwidth from 1 GHz to 4 GHz. Extensive behavioral modeling experiments on the collected datasets demonstrate that our method outperforms the existing methods in terms of normalized mean square error (NMSE). Further application in DPD scenarios proves that our method can effectively help improve the linearization performance of lightweight DPD models in terms of error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR).