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The quantification of carbon fluxes (CFs) is crucial due to their role in the global carbon cycle having a direct impact on Earth&#x0027;s climate. In the last years, considerable efforts have been made to scale CFs from eddy covariance (EC) data to the globe. In this work, a data-driven approach that exploits a multioutput Gaussian processes regression algorithm (<inline-formula><tex-math notation="LaTeX">$\mathcal{G}$</tex-math></inline-formula>-model) is proposed to jointly estimate gross primary production (GPP), terrestrial ecosystem respiration (TER), and net ecosystem exchange (NEE) at a global scale. The <inline-formula><tex-math notation="LaTeX">$\mathcal{G}$</tex-math></inline-formula>-model not only provides an estimate of the CFs but also an uncertainty. Moreover, it derives the three fluxes jointly preserving their physical relationship. The predictors are selected from a set of the moderate-resolution imaging spectroradiometer (MODIS) products available on Google Earth engine. The performance of the model revealed high accuracies (<italic>R</italic>² reaching 0.82, 0.69, and 0.80 in the case of GPP, NEE, and TER, respectively), and low root mean square errors (1.55 g m^&#x2212;2 d^&#x2212;1 in the case of GPP, 1.09 g m^&#x2212;2 d^&#x2212;1 for the NEE, and 1.14 g m^&#x2212;2 d^&#x2212;1 for TER) over the FLUXNET2015 data set at eight-day time scale. The GPP estimates provided by <inline-formula><tex-math notation="LaTeX">$\mathcal{G}$</tex-math></inline-formula>-model outperformed the MOD17A2 product, and a state-of-the-art GPP product (PML_V2) without using meteorological forcing data sets. The results reported mean annual amounts of 133.7, 114.8, and 18.9 Pg yr^&#x2212;1 for GPP, TER, and NEE, respectively, during the 2002&#x2013;2023 period. The proposed approach paves the way for the development of multioutput strategies that preserve the physical relationships among CFs in upscaling processes.