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<p>We present the coupling of the Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg version 4 (JSBACHv4) to the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. With JSBACH, the soil water bucket model in EMAC is replaced by a diffusive hydrological transport model for soil water that includes water storage and infiltration in five soil layers, preventing soil from drying too rapidly and reducing biases in soil temperature and moisture. A three-layer soil scheme is implemented, and phase changes in water in the soil are considered. The leaf area index (LAI) climatology in EMAC has been substituted with a phenology module calculating the LAI. Multiple land cover types are included to provide a state-dependent surface albedo, which accounts for the absorption of solar radiation by vegetation. Plant net primary productivity, leaf area index and surface roughness are calculated according to the plant functional types. This paper provides a detailed evaluation of the new coupled model based on observations and reanalysis data, including ERA5/ERA5-Land datasets, Global Precipitation Climatology Project (GPCP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Land surface temperature (LST), terrestrial water storage (TWS), surface albedo (<span class="inline-formula"><i>α</i></span>), net top-of-atmosphere radiation flux (Rad<span class="inline-formula">_TOA</span>), precipitation (precip), leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and gross primary productivity (GPP) are evaluated in particular. The strongest correlation (<span class="inline-formula"><i>r</i></span>) between reanalysis data and the newly coupled model is found for LST (<span class="inline-formula"><i>r</i>=0.985</span>, with an average global bias of <span class="inline-formula">−1.546</span> K), <span class="inline-formula"><i>α</i></span> (<span class="inline-formula"><i>r</i>=0.947</span>, with an average global bias of <span class="inline-formula">−0.015</span>) and Rad<span class="inline-formula">_TOA</span> (<span class="inline-formula"><i>r</i>=0.907</span>, with an average global bias of <span class="inline-formula">3.56</span> W m<span class="inline-formula">⁻²</span>). Precipitation exhibits a correlation with the GPCP dataset of <span class="inline-formula">0.523</span> and an average global bias of <span class="inline-formula">0.042</span> mm d<span class="inline-formula">⁻¹</span>. The LAI optimisation yields a correlation of <span class="inline-formula">0.637</span> with observations and a global mean deviation of <span class="inline-formula">−0.212</span>. FAPAR and GPP exemplify two of the many additional variables made available through JSBACH in EMAC. FAPAR and observations show a correlation of <span class="inline-formula">0.663</span>, with an average global difference of <span class="inline-formula">−0.223</span>, while the correlation for GPP and observations is <span class="inline-formula">0.564</span> and the average global difference is <span class="inline-formula">−0.001</span> kg carbon km<span class="inline-formula">⁻¹</span>. Benefiting from the numerous added features within the simulated land system, the representation of soil moisture is improved, which is critical for vegetation modelling. This improvement can be attributed to a general increase in soil moisture and water storage in deeper soil layers and a closer alignment of simulated TWS with observations, mitigating the previously widespread problem of soil drought. We show that the numerous newly added components strongly improve the land surface, e.g. soil moisture, TWS and LAI, while surface parameters, such as LST, surface albedo or Rad<span class="inline-formula">_TOA</span>, which were mostly prescribed according to climatologies, remain similar. The coupling of JSBACH brings EMAC a step closer towards a holistic comprehensive Earth system model and extends its versatility.</p>