Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification as well as their impacts on marine organisms and ecosystems. So far ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim at developing a sustainable production chain of observation-derived global ocean carbonate system datasets at high space-time resolution. As the start of the long-term objective, this study introduces a new global 0.25° monthly reconstruction, namely CMEMS-LSCE, for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (DIC), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple linear regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT. 1σ-uncertainty associated with these estimates is also provided. Here, σ stands for either ensemble standard deviation of pCO2 estimates or total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25°-resolution pCO2 product outperforms a coarser spatial resolution (1°) thanks to a higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal-open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-of-mean–square–deviation from observations less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (DIC), and pH. The global average 1σ-uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and DIC, and 0.4 % for pH relative to their global mean values. Both model-observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account in priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification.