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Urban vegetation mitigates carbon and provides ecosystem services. Quantifying these benefits relies on land surface models like JSBACH, but high-resolution long-term simulations are computationally heavy and too complex for practical applications. Machine learning emulators offer a computationally efficient alternative. Here, we present daily and monthly emulators for gross primary production (GPP) and net ecosystem exchange (NEE) of CO₂ for different plant functional types (PFTs) in Helsinki: deciduous and coniferous trees, lawn, and crops represented by 50/50 weight of cereal and agricultural grass. The emulators are trained on JSBACH simulations for 1991-2015 and evaluated for 2016-2024. Predictor variables are derived from daily air temperature, precipitation, and shortwave radiation.The emulators are based on gradient boosting models with automated hyperparameter optimization. We trained separate models for each target variable and PFT. To estimate the total value of a target variable for each 50 m × 50 m pixel in Helsinki, we combined PFT specific predictions weighted by the fractional coverage of each vegetation type within the pixel.Emulator performance was high across all plant functional types and for both carbon fluxes. The monthly emulator outperformed the daily emulator consistently, as demonstrated by higher explained variance and lower errors for both GPP and NEE. Although the monthly emulator smoothed out short-term variability, it still reproduced total annual GPP and NEE with a level of accuracy almost matching that of the daily emulator. The two machine learning emulators developed in this study achieved high levels of accuracy, enabling faster simulations than the original land surface model. The daily emulator provided more detailed information on how vegetation responds to different meteorological conditions. In contrast, the monthly emulator was better suited to urban planning, offering fast and reliable information on the carbon sequestration of various PFTs over extended periods, while reducing simulation time by over 95% compared to the daily emulator.