Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO₂ fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.