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Aiming to analyze the responses of urban greenland’s net ecosystem exchange (NEE) to the climatic controls and provide theoretical and technical support for carbon cycle simulation between land and atmosphere. In growing season, halfhourly daytime NEE based on eddy covariance flux data collected from 2013 to 2016 were simulated by XGBoost and back propagation artificial neural network (ANN) model. Moreover, the accuracy of model was evaluated by using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and index of agreement (IA). The experimental results showed that ANN model presented that seven input variables (photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (Ta), relative humidity (RH), soil temperature (Ts), wind speed (WS) and volumetric water content at 10cm depth) performed best, yielding R2 of 0.712, RMSE of 4.394μmol/(m2·s), MAE of 3.129μmol/(m2·s) and IA of 0.911 on train dataset, and R2 of 0.748, RMSE of 4.253μmol/(m2·s), MAE of 2.971μmol/(m2·s) and IA of 0.920 on test dataset. After considering the function and interaction among the factors, the importance score of each environmental factor was decreased in the following order: PAR, VPD, Ta, RH, Ts, WS and VWC10, otherwise Ts would be more important than RH. In particularly, after calculating the numerical partial derivatives of main climatic controls for each halfhourly point, the numerical partial derivatives of PAR showed the ecosystem quantum yield with the value of 0.087, and it also indicated that PAR was no longer a main impact factor when value was greater than 1200μmol/(m2·s). Besides, the numerical partial derivatives of VPD expressed that VPD could mainly inhibit the photosynthesis, and the higher VPD aggravated the inhibition of photosynthesis by affecting photosynthetic rate. Furthermore, the numerical partial derivatives of Ta demonstrated that the photosynthetic rate was increased bit by bit and made the photosynthetic rate overpass respiration rate gradually. According to the result, PAR, VPD and Ta played an important role in controlling the NEE of urban greenland ecosystem. Also, XGBoost and ANN could be capable in capturing NEE dynamics and simulating the NEE with high accuracy. Meanwhile, the present result provided instant insight in underlying ecosystem physiology.