Local severe convective storms significantly impact people’s lives and socio-economic development. Understanding the mechanism of severe convective storms and predicting the occurrence and development of local severe storms remains challenging. We investigate local severe convective storms’ environmental and thermodynamic characteristics in pre-convection environments by combining observations from China’s new-generation geostationary satellites (FY-4 series), which offer high spatial-temporal resolution, with numerical weather forecast products from the China Meteorological Administration (CMA) global forecast system (CMA-GFS). Furthermore, we explore how their changes impact the future development intensity of local convection. Results show a close association between cloud top cooling information from satellite observations and numerical prediction model variables, such as atmospheric instability and water vapour content, with convection storm occurrence and intensity. Changes in these factors closely relate to the future development intensity of local convection. The Storm Warning In Pre-convective Environment Version 2.0 (SWIPE-V2.0) system aims to predict the occurrence and intensity (weak, medium, and strong) of local severe storms in China. Established using machine-learning techniques, SWIPE-V2.0 employs the brightness temperature threshold method and area threshold method to identify the local convective cloud. Meanwhile, it uses the overlapping and optical flow methods to track the movement of the local convective cloud. The machine-learning model uses the CLDAS (Global Land Data Assistance System) data of the multi-source precipitation fusion dataset, obtained half an hour to one hour after the cloud cluster, as the training tag. Independent validation results reveal SWIPE-V2.0’s strong performance in early warning for local convective storms, with recognition rates of 0.5-0.85 for cases with precipitation below 8 mm/h and 0.69-0.91 for cases with precipitation above 8 mm/h in the rainy season across six different regions. In the non-rainy seasons, across the same regional spread, recognition rates are 0.53-0.98 for cases with precipitation below 8 mm/h and 0.77-0.99 for cases with precipitation above 8 mm/h. Early warning results from SWIPE-V2.0 on real-time local convection systems demonstrate its potential for near real-time applications, while also indicating its useful role in understanding the environmental factors associated with local severe storms across various weather regimes. Currently, we are utilising SWIPE-V2.0 in real-time applications.