Abstract Convection‐permitting modeling has become a cornerstone for improving representation of land‐atmosphere processes, predicting weather extremes, and creating long‐term hydrometeorological data sets. The convection‐permitting Weather Research and Forecasting (WRF) modeling with the NoahMP land‐surface scheme is widely used for kilometer‐scale hydroclimatic applications. We identify persistent large warm biases (up to ∼8°C) in daily minimum surface air temperature (Tmin) in convection‐permitting WRF/NoahMP simulations across the western, central, and southeastern US. Motivated by the potential of soil organic matter (SOM) to mitigate nighttime warm bias, we implement a physical representation of this previously missing component and assess its effects on land‐surface fluxes and surface air temperature. Including SOM significantly changes surface soil thermal properties and reduces Tmin warm bias (by 75%) by mitigating the overestimated diurnal amplitude of ground heat flux (by 29%). This offers a physics‐based pathway to improve future kilometer‐scale simulations.