## 5 Conclusions  
In this study, we applied two machine learning algorithms, RF and XGBoost, to simulate NEE in major biomes across the globe. We found that the XGBoost has a better model performance than the RF with a 6-62 times higher computational efficiency. The robustness of the two methods was also tested for different extreme climate conditions and the different numbers of training samples. Both XGBoost and RF reflect the difference of prediction between extreme condition samples and normal samples well. The training sample size impact on model performance varied by biome. Biomes with better model performance (DBF and ENF) were easier to achieve the first stability with a smaller NTS and continued to optimize with increasing NTS, while the opposite applied to GRA and CRO biomes. In general, a minimum of 8 years of daily training dataset will lead to feasible model prediction for various biomes. The variables used for the model training include 10 meteorological variables, which is slightly less than other studies. Both RF and XGBoost algorithms produced feasible results, which indicates that the variables other than those used in this study have slight contribution to the NEE prediction. Given the strengths of XGBoost, it could have good implications on upscaling NEE prediction from the site to a regional area in the future, e.g., based on grouping of samples under normal and extreme climate conditions. It also provides the potential application for estimating NEE from satellite-derived data, which should be very helpful for regional and global scale. The following are available online at [[https://www.mdpi.com/article/10.3390/rs13122242/s1](https://www.mdpi.com/article/10.3390/rs13122242/s1)]([https://www.mdpi.com/article/10.3390/rs13122242/s1](https://www.mdpi.com/article/10.3390/rs13122242/s1)). Table S1 Hyper-parameters used for the models and the initial settings with randomized SearchCV method, Table S2 List of sites with extreme conditions. Acronyms: MAT, mean annual temperature; MMT, multi-year mean temperature; AP, annual precipitation; MMP, multi-year mean precipitation; SD, standard deviations of temperature or precipitation for thelisted sites; DBF, deciduous broadleaf forest; EBF, evergreen broadleaf forest; ENF, was evergreen needleleaf forest; MF, mixed forest; GRA, grassland; CRO, cropland; SAV, savannas, Table S3 Number of training and testing samples used for quantitative analysis of model performance, Figure S1 Permutation-based importance of variables in predicting NEE by (a) RF and (b) XGBoost (b) at biome level (NEE: Net ecosystem exchange; TA: Air temperature; SW_IN: Shortwave radiation, incoming; LW_IN: Longwave radiation, incoming; VPD: Vapor pressure deficit; PA: Atmospheric pressure; P: Precipitation; WS: Wind speed; NETRAD: Net radiation; TS: Soil temperature in the top soils; SWC: Soil water content in the top soils (0-5 cm), Figure S2 Feature importance of variable in predicting NEE by (a) Random Forest and (b) XGBoost methods with the mixed forest as an example. The colors of each grid reflect the value of feature importance, darker color indicates more important, Figure S3 Mutation detection performed on the evaluation results of Random Forest corresponding to the Figure 4e-h by the BG segmentation algorithm. The x axis and y axis values are the same as in Figure 4e-h. The black vertical dash lines represent mutation points, Figure S4 Correlation between training samples and training duration for (a) RF and (b) XGBoost. Conceptualization, X.X. ; methodology, J.L., F.Y., and X.S. ; software, J.L. ; validation, J.L. and Y.Z. ; resources, C.S. and Y.G. ; data curation, J.L. ; writing--original-draft preparation, J.L. ; result interpretation: all; writing--review and editing, X.X., F.Y., N.W., Y.Z., L.Z., X.Z., Z.G., Y.S. and J.Z. ; visualization, J.L. ; supervision, X.X. ; project administration, F.Y. and X.X. ; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.