The lack of a standardized database of eddy covariance observations has been an obstacle for data‐driven estimation of terrestrial CO₂ fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data‐driven estimation of gross primary productivity (GPP) and net ecosystem CO₂ exchange (NEE). Data‐driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site‐level evaluation of the estimated CO₂ fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r² = 0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor‐based Sun‐induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r² = 1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere‐land CO₂ fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO₂ fluxes from SVR‐NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO₂ fluxes. These data‐driven estimates can provide a new opportunity to assess CO₂ fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

Archival data of monthly air temperature and precipitation series were used to investigate climate change trends and characteristics during 1960–2000 at 19 stations along Lancang River from the north to the south, in the mountainous Himalayan region of southwest China. The magnitude of a trend was estimated using Sen's Nonparametric Estimator of Slope approach. The station significance of a trend was assessed by the MK test. Over the observation period of 41 years, mean annual air temperature increased at the rate of 0.01°C/yr to 0.04°C/yr in 12 stations at the significance level α = 0.01. The changes in precipitation in different areas are very dissimilar and complex. Mean annual precipitation that decreased from −2.86mm/yr to −5.29mm/yr at 3 stations, and mean annual precipitation that increased from 5.77mm/yr and 7.44mm/yr at 2 stations, were statistically significant at the significance level α = 0.05. The lower reaches of Lancang River experienced much more severe temperature increase, preci...