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Intense lightnings and hailfall are both hallmarks of severe convective storms, but they are rarely associated with long-term climate studies. The main reason is the lack of long-term observations. But recently, the term “extreme weather” is often cited in media as a possible dire consequence of worsening global warming in the foreseeable future, however, it is often ambiguous of what type of extreme weather they are referring to. Most recent future predictions are done by performing climate model simulations under certain global warming scenarios. However, the resolution of the current generation climate models is not good enough to resolve individual storm system let alone pinning down the physical mechanisms. This ambiguity in physical mechanism impedes the better understanding of the nature of these extreme weather/climate events. In this paper, we present a unique study to show that severe storms with intense lightnings and hailfall are indeed connected with long-term climate change. In this study, we utilize the meteorological series derived from the REACHES climate database compiled from Chinese historical documents (Wang et al., 2018; 2024, Nature: Scientific Data) and extract temperature, lightning and hailfall times series for the period of 1368-1911 (a 543-year period) and performed correlation analysis among them. Our results show that there exists strong negative correlation between either temperature-lightning or temperature-hailfall pair. This means that severe convective storms as manifested by intense lightning and heavy hailfall occurred in colder climate periods. The correlation coefficients for both pairs are close to -0.9 for the 30-year moving average series. Such a stable correlation over such a long period indicates that this cannot be a random coincidence but there must be persistent physical mechanisms involved. The temperature-lightning correlation is stronger, indicating that the climate physical state must be closely connected with atmospheric electricity.We have made further analyses by looking into different seasons to understand the seasonal variations of the above negative correlation. We will also investigate the regional variations of the above relation. These results will shed more lights to the physical mechanisms responsible for this phenomenon. We will also utilize physics-based storm model simulation results to understand the possible dynamical processes involved.