Robustness is significant for generative image watermarking, typically achieved by injecting distortion-invariant watermark features. The leading paradigm, \emph{i.e.}, inversion-based framework, excels against non-geometric distortions but struggles with geometric ones. Due to the complexity of geometric distortions, finding universally geometric-invariant features is challenging, and it is not clear whether such invariant representation exists. To address this, we propose SynTag, a \textbf{syn}chronization \textbf{tag} injection-based method that enhances geometric robustness in inversion-based schemes. Instead of seeking invariant representations, we embed a sensitive template feature alongside the watermarking features. This template evolves with geometric distortions, allowing us to reconstruct the distortion trajectory for correction before extraction. Focusing on latent diffusion models, we fine-tune the VAE decoder to inject the invisible SynTag feature, pairing it with a prediction network for extraction and correction. Additionally, we introduce a dither compensation mechanism to further improve correction accuracy. SynTag is highly compatible with existing inversion-based methods. Extensive experiments demonstrate a significant boost in geometric distortion robustness while maintaining resilience against non-geometric distortions.