## 5 Conclusions  
This study establishes the first deep learning-based Ionospheric Vertical Decomposition Framework (IVDF) that combines global coverage (by leveraging globally assimilated TEC), integrated space weather indices (AE/SYM-H/F10.7), and capabilities supporting real-time applicability for the high-precision mapping of macroscale TEC to vertically layered electron density (Ne) profiles spanning 60-800 km. The marked performance improvements validate the potential of data-driven approaches fused with space physics parameters, while also revealing the need to deepen \"data-physics\" synergistic modeling mechanisms under complex space weather events. This work not only advances a novel technical pathway for ionospheric research but also lays critical foundations for near-space environment monitoring and geomagnetic hazard warning systems.  
Conceptualization, [PERSON] ([PERSON]) and [PERSON]; methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); software, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and [PERSON]; visualization, [PERSON] ([PERSON]); supervision, [PERSON]; project administration, [PERSON], C.F. and Y.G.; funding acquisition, [PERSON] and J.W. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the China Meteorological Administration \"Space Weather Monitoring and Alerting\" Key Innovation Team (CMA2024 ZD01), the \"Ionospheric Forecast and Alerting\"Youth Innovation Team (CMA2024 QN09), and the Jiangxi Provincial Natural Science Foundation (20224 ACB211005).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** In this study, the training and validation data used are publicly available on the respective websites. The IGS-TEC hourly data are sourced from [[https://igs.org/products/](https://igs.org/products/)]([https://igs.org/products/](https://igs.org/products/)) (accessed on 15 March 2025) and [[https://spdf.gsfc.nasa.gov/pub/data/gps/](https://spdf.gsfc.nasa.gov/pub/data/gps/)]([https://spdf.gsfc.nasa.gov/pub/data/gps/](https://spdf.gsfc.nasa.gov/pub/data/gps/)) (accessed on 16 March 2025). The AE index is obtained from [[https://wdc.kugi.kyoto-u.ac.jp/ae_provisional/index.html](https://wdc.kugi.kyoto-u.ac.jp/ae_provisional/index.html)]([https://wdc.kugi.kyoto-u.ac.jp/ae_provisional/index.html](https://wdc.kugi.kyoto-u.ac.jp/ae_provisional/index.html)) (accessed on 16 March 2025). The SYM-H index is derived from [[https://wdc.kugi.kyoto-u.ac.jp/dst_final/index-j.html](https://wdc.kugi.kyoto-u.ac.jp/dst_final/index-j.html)]([https://wdc.kugi.kyoto-u.ac.jp/dst_final/index-j.html](https://wdc.kugi.kyoto-u.ac.jp/dst_final/index-j.html)) (accessed on 17 March 2025). The F10.7 data are acquired from [[http://eng.sepc.ac.cn/F107](http://eng.sepc.ac.cn/F107) Index.php]([http://eng.sepc.ac.cn/F107](http://eng.sepc.ac.cn/F107) Index.php) (accessed on 16 March 2025). The COSMIC satellite observation data are available at [[https://data.cosmic.ucar.edu/gnss-ro/](https://data.cosmic.ucar.edu/gnss-ro/)]([https://data.cosmic.ucar.edu/gnss-ro/](https://data.cosmic.ucar.edu/gnss-ro/)) (accessed on 15 March 2025).  
**Acknowledgments:** We thank organizations that make their data public. Details are in the Data Availability Statement.  
**Conflicts of Interest:** The authors declare no conflicts of interest.