As the world’s most widely cultivated fruit, citrus in China is increasingly suffering from ongoing climate change, which affects the sustainability of agricultural systems and social economy. In this study, we linked climate factors to citrus quality and yield and established projection models to elucidate the impact of future climate change. Then, we used the ensemble mean of 19 Coupled Model Intercomparison Project 6 (CMIP6) models to project the 2021–2040 and 2041–2060 climate changes relative to the historical baseline 1995–2014 period under different shared socioeconomic pathways scenarios (SSP2-4.5, SSP5-8.5). The results show that the monthly mean diurnal temperature range in July had the greatest influence on quality, and monthly mean temperature in October, monthly mean relative humidity in October, monthly mean minimum temperature in November and monthly mean maximum temperature in September had the greatest influence on yield at the growth and ripening stages. Moreover, the quality and yield of citrus present different characteristics in terms of change in cultivation areas in the future. The quality of Sichuan, Zhejiang and Fujian Provinces in China will become significantly better, however, Hubei, Guangdong and Guangxi Provinces it will become worse. Surprisingly, yield will increase in all plantations due to future suitable climate conditions for citrus growth and ripening.

1. Carbon sequestration is a key ecosystem service provided by forests. Inventory data based on individual trees are considered to be the most accurate method for estimating forest productivity. However, the estimations of forest photosynthesis from inventory data remain understudied, particularly when considering the growth and development of individual trees under the background of global change.2. Here, we used the leaf growth process with phenology and non-structural carbohydrates (NSC) storage to revise an individual-tree-based carbon model, FORCCHN. This model couples leaf development and biomass to quantify gross primary productivity (GPP) in the forests, where growth is decoupled from photosynthesis in daily step. The model was initialized with inventory-based forest data rather than the more widely used satellite-based data.3. We tested the model against measured above-ground woody biomass, growth of leaf biomass, daily gross ecosystem exchange (GEE) and yearly GEE at five representative forest sites in the Northern Hemisphere. We also compared the results from the original model and the revised model at five forest sites. Including leaf growth dynamics and inventory-based initialization improved the predicted performance (r 2 ) of GPP by an average of 33%. Synthesis and applications.Our results suggest that the appropriate vegetation data sources (i.e. inventory or satellite selection) and the effective predictions of the growth process should be considered when developing future carbon cycle models and forest carbon estimation options. Applying and improving such carbon models to evaluate carbon sequestration is an important part of forest carbon sink management.

The diurnal temperature range (DTR) is an important meteorological component affecting maize yield. The accuracy of climate models simulating DTR directly affects the projection of maize production. We evaluate the ability of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models to simulate DTR during 1961–2014 in maize cultivation areas with the observation (CN05.1), and project DTR under different shared socioeconomic pathway (SSP) scenarios. The root mean square error (RMSE), standard deviation (SD), Kling-Gupta efficiency (KGE) and comprehensive rating index (CRI) are used in the evaluation of the optimal model. The results show that CMIP6 models can generally reproduce the spatial distribution. The reproducibility of the annual average DTR in the maize cultivation areas is better than that in China but lower for the maize-growing season. The optimal model (EC-Earth3-Veg-LR) is used in the projection. Under the two SSPs, the DTR decreases compared with the historical period, especially in Northwest and North China. The DTR under SSP245 remains unchanged (annual) or increases slightly (growing season) during 2015–2050, while a significant decreasing trend is observed under SSP585. This highlights the importance of evaluating DTR in maize cultivation areas, which is helpful to further improve the accuracy of maize yield prediction.

Winter wheat is widely planted in China. The changes of winter wheat yield and quality are related to the food security of human society. Climate change has an important impact on the yield and quality of winter wheat. Diurnal temperature range (DTR) is an important factor affecting the yield and protein content of winter wheat. Furthermore, climate model is one of the main sources of error in crop model simulations of yields. Therefore, how to improve the accuracy of climate data has become an important concern for scholars.Previous model evaluations for the entire country or region cannot answer which model is suitable for the estimation of future winter wheat yield. Therefore, we evaluated the ability of climate models to simulate DTR within the range of winter wheat growing regions in China to identify the most suitable climate models for winter wheat yield and quality projections. The results show that CMIP6 models can basically reproduce the DTR of winter wheat-growing regions in China, but there are discrepancies in the simulations between nationwide and winter wheat-growing regions. EC-Earth3-Veg has the best simulation of climate DTR for wheat-growing regions (TS=0.848) and nationwide (TS=0.842), and ACCESS-CM2 has the strongest ability to simulate the annual growing season DTR (TS=0.46). In summary, in the estimation of future winter wheat yield, attention should be given to the selection of models suitable for the actual growing regions and the growing seasons of winter wheat.

Diurnal temperature range (DTR) is an important meteorological component affecting the yield and protein content of winter wheat. The accuracy of climate model simulations of DTR will directly affect the prediction of winter wheat yield and quality. Previous model evaluations for worldwide or nationwide cannot answer which model is suitable for the estimation of winter wheat yield. We evaluated the ability of the coupled model intercomparison project phase 6 (CMIP6) models to simulate DTR in the winter wheat growing regions of China using CN05 observations. The root mean square error (RMSE) and the interannual varibility skill score (IVS) were used to quantitatively evaluate the ability of models in simulating DTR spatial and temporal characteristics, and the comprehensive rating index (CRI) was used to determine the most suitable climate model for winter wheat. The results showed that the CMIP6 model can reproduce DTR in winter wheat growing regions. BCC-CSM2-MR simulations of DTR in the winter wheat growing season were more consistent with observations. EC-Earth3-Veg simulated the climatological DTR best in the wheat growing regions (RMSE=0.848). Meanwhile, the evaluation for climatological DTR in China is not applicable to the evaluation of DTR in winter wheat growing regions, and the evaluation for annual DTR is not a substitute for the evaluation for winter wheat growing season DTR. Our study highlights the importance of evaluating winter wheat growing regions' DTR, which can further improve the ability of CMIP6 models simulating DTR to serve the research of climate change impact on winter wheat yield.Not applicable.