Eighteen lightning flash rate parameterization schemes (FRPSs) were investigated in a Weather Research and Forecasting model coupled with chemistry cloud‐resolved simulation of the 29–30 May 2012 supercell storm system observed during the Deep Convective Clouds and Chemistry (DC3) field campaign. Most of the observed storm's meteorological conditions were well represented when the model simulation included both convective damping and lightning data assimilation techniques. Newly‐developed FRPSs based on DC3 radar observations and Lightning Mapping Array data are implemented in the model, along with previously developed schemes from the literature. The schemes are based on relationships between lightning and various kinematic, structural, and microphysical thunderstorm characteristics (e.g., cloud top height, hydrometeors, reflectivity, and vertical velocity) available in the model. The results suggest the model‐ simulated graupel and snow/ice hydrometeors require scaling factors to more closely represent proxy observations. The model‐simulated lightning flash trends and total flashes generated by each scheme over the simulation period are compared with observations from the central Oklahoma Lightning Mapping Array. For this supercell system, 13 of the 18 schemes overpredicted flashes by >100% with the group of FRPSs based on storm kinematics and structure (particularly updraft volume) performing slightly better than the hydrometeor‐based schemes. During the storm's first 4 hr, the upward cloud ice flux FRPS, which is based on the combination of vertical velocity and hydrometeors, well represents the observed total flashes and flash rate trend; while, the updraft volume scheme well represents the observed flash rate peak and subsequent sharp decline in flash rate. Plain Language Summary Accurate lightning forecasts are important for daily activities. They are also important because lightning produces nitrogen oxide, which affects the distribution of atmospheric trace gases that have significant roles in influencing our climate (e.g., ozone). Lightning is recreated in weather models using already determined relationships between observed lightning and thunderstorm characteristics (e.g., hydrometeors and vertical velocity). Lightning prediction is also highly dependent on how well the model represents current conditions, like thunderstorm location and strength. Eighteen lightning‐thunderstorm relationships, or schemes, were investigated in a cloud‐resolved Weather Research and Forecasting model coupled with chemistry to simulate the 29–30 May 2012 supercell storm system observed during the Deep Convective Clouds and Chemistry (DC3) field campaign and to identify the best scheme associated with the event. Modifications to the model‐simulation were first required to better represent the observed convection