Drought risk analysis can help improve disaster management techniques, thereby reducing potential drought risk under the impacts of climate change. This study analyses observed and model-simulated spatial patterns of changes in drought risk in vulnerable eco-regions in China during 1988–2017 and 2020–2050 using an analytic hierarchy process (AHP) method. To perform a risk assessment and estimation of a drought disaster, three subsystems—namely hazard, vulnerability and exposure—are assessed in terms of the effects of climate change since the middle of the 21st century: (i) Hazards, represented by climate anomalies related to the drought process, such as changes in rainfall averages, temperature averages and evaporation averages; (ii) vulnerability, encompassing land use and mutual transposition between them; (iii) exposure, consisting of socioeconomic, demographic, and farming. The results demonstrated that high hazards continue to be located in the arid zone, high vulnerability levels occur in the Junggar Basin and Inner Mongolia Plateau, and high exposure levels occur Loess Plateau and southern coastal area. In this way, the results provide exhaustive measures for proactive drought risk management and mitigation strategies.

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

Climate change has increased the frequency of extreme droughts and floods in China, threatening agricultural production and food security. However, the impacts of these extreme precipitation events on crops (maize, wheat, and rice) during key growth stages remain poorly understood. To address this, we developed a three-step analytical framework: First, we used transpiration data to identify critical crop growth stages across China. Then, we applied a 10-day standardized precipitation evapotranspiration index (SPEI) to quantify drought and extreme wetness conditions during each growth phase. Finally, we integrated these data into an XGBoost model to assess the relationship between extreme weather and crop yield fluctuations. The results show that maize is most sensitive to water variability during both development and mid-season stages, while wheat is particularly vulnerable to drought during development and rice is mainly affected by water stress during the mid-season. Extreme drought risks are highest in the Northeast Plain, North China Plain, and southern China, while extreme wetness risks are concentrated in the middle and lower Yangtze River basin and southeastern coastal regions. Notably, extreme drought risks are significantly more pronounced than those associated with extreme wetness. These findings highlight the urgent need for targeted agricultural strategies to promote sustainable agricultural development.

In the context of global climate change and urban expansion, extreme urban weather events occur frequently and cause significant social problems and economic losses. To study the climate risks associated with rapid urbanization in the global context of climate change, the vulnerability degree of urban agglomeration is constructed by the Grey Model (GM (1, 1)). Based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6) data sets SSP1-2.6, SSP2-4.5, and SSP5-8.5, drought, heat wave, and flood hazards under different emission scenarios are calculated. The vulnerability degree of the urban agglomeration and the climate change hazard were input into the climate change risk assessment model to evaluate future climate change risk. The analysis results show regional differences, with the Beijing–Tianjin–Hebei urban agglomeration having good urban resilience, the Yangtze River Delta urban agglomeration having slightly higher overall risk, and the Pearl River Delta urban agglomeration having the highest relative risk overall. On the whole, the higher the emission intensity is, the greater the risk of climate change to each urban agglomeration under different emission scenarios.

As global warming issues become increasingly serious, grain yield and socioeconomic development have been seriously threatened. The key to ensuring grain yield is to recognize the risks caused by climate change. In this paper, the trends of temperature and precipitation over the next thirty years in China are analysed using CMIP6 under the SSP1–2.6, SSP2–4.5 and SSP 5–8.5 climate scenarios. The resilience indicators of grain yield are proposed for the first time. We find that the higher the emission concentration is, the greater the temperature increase will be and further northward the precipitation belt will move. Meanwhile, the resilience varies across different climate zones. The temperate monsoon climate zone has a stronger resilience to adapt to climate change compared to that of other areas. The resilience of the temperate continental and plateau alpine climate zones are moderate. However, the resilience of the subtropical and tropical monsoon climates zones are poor.