PMGC-SimVP: Parametric Multi-scale Gated Convolution for Global Ionospheric TEC Prediction

Spatiotemporal prediction of complex dynamical systems de-pends not only on historical observations but also on evolving exter-nal control parameters. Crucially, for ionospheric Total Electron Con-tent, existing concatenation-based conditioning fails to capture region-dependent responses driven by space weather.We propose PMGC-SimVP, a spatiotemporal prediction framework withParametric Multi-scale Gated Convolution. PMGC injects external con-trol parameters into local dynamics via latitudinally structured mod-ulation, enabling explicit modeling of non-stationary evolution and re-gional heterogeneity. Built upon a purely convolutional backbone anda low-rank temporal adapter, PMGC-SimVP remains computationallyex001Ecient.We evaluate PMGC-SimVP on global TEC forecasting, where dynam-ics are strongly driven by space weather and exhibit pronounced lati-tudinal variability. The proposed method achieves consistent improve-ments over recent state-of-the-art models, including Transformer-basedand frequency-domain approaches, across overall evaluation settings andduring geomagnetic disturbances. Experiments on generic video predic-tion benchmarks further verify the robustness of the proposed backboneeven without parameter injection.