To investigate global and regional gross primary productivity (GPP) and its sources of uncertainties, widely used model structures of light use efficiency models are integrated to build a meta-model. Meteorological reanalysis data and remote sensing data are combined to estimate GPP, and a systematical and quantitative uncertainty analysis is conducted based on the ANOVA approach. Results show that: 1) the meta-model results correspond well with the upscaling of eddy-covariance measurements (FLUXCOM) GPP with a Pearson correlation coefficient of 0.97 and root mean square error of 24.36 gC/(m²·month) and outperforms any single combination of model structure. 2) Photosynthetically active radiation (PAR), water-related data and water regulation scalar (Ws) are the three main sources of uncertainties for global GPP estimates, contributing 41.73%, 26.79% and 23.82% respectively to total variance. 3) Sources of uncertainties of regional GPP depend on environmental conditions. For arid areas, Ws is the dominant contributor (over 80%). In cold areas, temperature regulation scalar (Ts) introduces over 40% of uncertainty. The findings not only highlight the necessity to reduce uncertainty of PAR and water-related data to reduce uncertainty in global and regional GPP estimates, but also point out the importance of improving performances of Ws and Ts algorithms under extreme environmental conditions.

Based on the Earth System Model, greenhouse gas emissions and atmospheric composition of CMIP6 and population data, the correlation between the change of regional mean temperature and extreme climatic indices was studied. Three climatic indices over East Asia under nine SSPs-RCPs scenarios were predicted, and the variation and attribution of population exposure to high temperature were analyzed. The results indicate that 1) there is a robust correlation between the change of global mean temperature and regional extreme climatic indices, which can be used to predict the latter in the future. 2) East Asia will experience increasing risk of extreme climate event in the future decades under SSP2-4.5, SSP4-6.0, SSP3-LowNTCF, SSP3-7.0-Baseline and SSP5-8.5-Baseline scenarios. But taking mitigation measures in advance could reduce such risk significantly. 3) Future population exposure to high temperature of three typical regions of East Asia, which is affected by both climate and population factors, changes dynamically over time and regions. Under most scenarios, the effects of climate and population factors are gradually weakening and strengthening, respectively. The population exposure to high temperature of Southern China is significantly higher than that of Southwest and Central China, and the relative contribution of climate factors is also higher than that of these two regions.