Abstract In this work, we characterized the occurrences and conditions before the initiations of mesoscale convective systems (MCSs) in the central United States, using 15 years of observations and convection-permitting climate model simulations. The variabilities of MCSs in summer were obtained using high-resolution (4 km) observation data [Stage-IV (stIV)] and ECMWF Re-Analysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) Model simulations (E5RUN). MCSs were identified using the object tracking algorithm MODE-time domain (MTD). MTD-determined MCSs were divided into daytime short-lived MCSs (SLM12), daytime long-lived MCSs (LLM12), nighttime short-lived MCSs (SLM00), and nighttime long-lived MCSs (LLM00). E5RUN showed skill to simulate MCSs by obtaining similar statistics in occurrences, areal coverages, and propagation speeds compared to those of stIV. We calculated the 15 parameters using sounding data from E5RUN before an MCS was initiated (−1, −3, −6, and −9 h) at each location of an MCS. The parameters were tested to figure out the significance of predicting the longevities of MCSs. The key findings are 1) LLM12 showed favorable thermodynamic variables compared to that of SLM12 and 2) LLM00 showed significant conditions of vertically rotating winds and sheared environments that affect the longevity of MCSs. Moreover, storm-relative helicity of 0–3 km, precipitable water, and vertical wind shear of 0–6 km are the most significant parameters to determine the longevities of MCSs (both daytime and nighttime MCSs). Significance Statement The purpose of this study is to understand the features of mesoscale convective systems (MCSs) in observational data and convection-permitting climate model simulations. We tested long-term simulations using new forcing data (ERA5) to see the benefits and limitations. We designed a novel approach to obtain the distributions of meteorological parameters (instead of obtaining one value for one event of MCS) before initiations of MCSs to understand preconvective conditions (times from −9 to −1 h from initiation). We also divided MCSs into daytime/nighttime and short-/long-lived MCSs to help predict MCSs longevity considering the initiation times. Our results provide hints for the forecasters to predict MCS longevity based on preconvective conditions from parameters discussed in this work.