Accurate assessment of land–atmosphere (L–A) coupling in climate models requires high-frequency data output. The study outlines the need for sub-daily data to capture the complex interactions between the land and atmosphere, which span various timescales from minutes to seasons. The authors highlight the importance of local L–A coupling (LoCo) metrics in understanding convective processes, climate feedbacks, and extreme weather events. They propose a data request that includes hourly 3D atmospheric profiles, hourly 3D soil profiles, and hourly 2D fields of surface pressure, boundary layer height, precipitation, and other relevant variables. This data request aims to enable more accurate modeling and comparison with observational data, improving the understanding and prediction of L–A interactions. The study emphasizes the need for high-frequency data to capture the rapid changes in surface fluxes and boundary layer dynamics, which are crucial for assessing the strength and nature of L–A coupling. The proposed data request is divided into three categories: high-frequency archival of surface variables, archival of data at lower-tropospheric pressure levels, and code modifications for internal computation of mean properties within and above the boundary layer. The authors conclude that increasing the time resolution of model output is essential for fully understanding and predicting L–A processes and for improving the resilience of ecosystems and human societies to changing climatic conditions.Accurate assessment of land–atmosphere (L–A) coupling in climate models requires high-frequency data output. The study outlines the need for sub-daily data to capture the complex interactions between the land and atmosphere, which span various timescales from minutes to seasons. The authors highlight the importance of local L–A coupling (LoCo) metrics in understanding convective processes, climate feedbacks, and extreme weather events. They propose a data request that includes hourly 3D atmospheric profiles, hourly 3D soil profiles, and hourly 2D fields of surface pressure, boundary layer height, precipitation, and other relevant variables. This data request aims to enable more accurate modeling and comparison with observational data, improving the understanding and prediction of L–A interactions. The study emphasizes the need for high-frequency data to capture the rapid changes in surface fluxes and boundary layer dynamics, which are crucial for assessing the strength and nature of L–A coupling. The proposed data request is divided into three categories: high-frequency archival of surface variables, archival of data at lower-tropospheric pressure levels, and code modifications for internal computation of mean properties within and above the boundary layer. The authors conclude that increasing the time resolution of model output is essential for fully understanding and predicting L–A processes and for improving the resilience of ecosystems and human societies to changing climatic conditions.