Abstract
<jats:p>Abstract. Process-level modeling is central to diagnosing and improving atmospheric parameterizations in Earth system models, yet continental single-column and convection-permitting process configurations are often run with prescribed surface fluxes, limiting realism and consistency with fully coupled global simulations. Here we develop, document, and demonstrate a reproducible workflow to enable fully interactive land–atmosphere coupling in both the E3SM single-column model (SCM) and the doubly periodic configuration of SCREAM (DP-SCREAM). The approach leverages a multi-year offline-forced E3SM/ELM integration to provide spun-up land initial conditions and introduces an offline preprocessing method that generates DP-SCREAM–compatible land restart files by extracting and replicating the appropriate ELM grid cell (including its landunit/column/PFT hierarchy) across the doubly periodic domain, avoiding intrusive modifications to ELM I/O. Using daily-initialized two-day hindcast integrations, we apply this framework to two years over GoAmazon and ten years over the Southern Great Plains, and to targeted short process-level cases (CASS and GoAmazon single/double pulse). Interactive land coupling is required to reproduce key near-surface thermodynamic biases characteristic of global coupled simulations, including warm 2-m temperature biases and the elimination of spurious persistent nocturnal fog layers that arise in prescribed-flux configurations. In contrast, prominent cloud and convection process-level biases identified previously (i.e. insufficient deepening of shallow convection, deficits of mid-level congestus, and weak convective organization) are largely insensitive to prescribed versus interactive surface flux treatments, indicating that they are driven primarily by atmospheric model physics and/or dynamics. These results provide both a community-ready capability for coupled land process-level simulations in E3SM and practical guidance on when interactive versus prescribed surface fluxes are appropriate for process-level model evaluation and development.</jats:p>