In this study, the instability of extreme temperatures is defined as the degree of perturbation of the spatial and temporal distribution of extreme temperatures, which is to show the uncertainty of the intensity and occurrence of extreme temperatures in China. Based on identifying the extreme temperatures and by analyzing their variability, we refer to the entropy value in the entropy weight method to study the instability of extreme temperatures. The results show that TXx (annual maximum value of daily maximum temperature) and TNn (annual minimum value of daily minimum temperature) in China increased at 0.18 °C/10 year and 0.52 °C/10 year, respectively, from 1966 to 2015. The interannual data of TXx’ occurrence (CTXx) and TNn’ occurrence (CTNn), which are used to identify the timing of extreme temperatures, advance at 0.538 d/10 year and 1.02 d/10 year, respectively. In summary, extreme low-temperature changes are more sensitive to global warming. The results of extreme temperature instability show that the relative instability region of TXx is located in the middle and lower reaches of the Yangtze River basin, and the relative instability region of TNn is concentrated in the Yangtze River, Yellow River, Langtang River source area and parts of Tibet. The relative instability region of CTXx instability is distributed between 105° E and 120° E south of the 30° N latitude line, while the distribution of CTNn instability region is more scattered; the TXx’s instability intensity is higher than TNn’s, and CTXx’s instability intensity is higher than CTNn’s. We further investigate the factors affecting extreme climate instability. We also find that the increase in mean temperature and the change in the intensity of the El Niño phenomenon has significant effects on extreme temperature instability.

Hydrological changes combined with earthquakes easily trigger secondary disasters, including geological hazards. The secondary hazard of precipitation is the main disaster type in the Longmenshan Area (China). The 2008 Wenchuan earthquake caused more than 60,000 landslides, severely affecting rural households. This study aimed to answer two questions: (1) How did households adapt to the landslide-prone post-earthquake environment? (2) How will the households’ adaptation strategies change if landslide frequency changes? Different post-disaster adaptation strategies of households in Longmenshan Town, Sichuan, China were identified through a questionnaire survey and then clustered into groups based on similarity using a K-means algorithm. Afterward, a gradient boosting decision tree (GBDT) was used to predict change in adaptation strategies if there was a change in the frequency of landslides. The results show that there are three types of landslide adaptation strategies in the study area: (1) autonomous adaptation; (2) policy-dependent adaptation; and (3) hybrid adaptation, which is a mixture of the first two types. If the frequency of landslides is increased, then around 5% of households previously under the autonomous adaptation type would be converted to policy-dependent and hybrid adaptation types. If the frequency of landslides is reduced, then around 5% of households with policy-dependent adaptation strategies would be converted to the autonomous adaptation type. This exploratory study provides a glimpse of how machine learning can be utilized to predict how adaptation strategies would be modified if hazard frequency changed. A follow-up long-term study in Longmenshan Town is needed to confirm whether the predictions are indeed correct.

Quantitatively predicting the impacts of climate change on water demands of various crops is essential for developing measures to ensure food security, sustainable agriculture, and water resources management, especially in arid regions. This study explored the water footprints (WFs) of nine major crops in the middle and downstream areas of Shule River Basin, Northwest China, from 1989 to 2020 using the WF theory and CROPWAT model and predicted the future WFs of these crops under four emission and socio-economic pathway (SSPs-RCPs) scenarios, which provides scientific support for actively responding to the negative impacts of climate change in arid regions. Results indicated: (1) an increasing trend of the overall crop WF, with blue WF accounting for 80.31–99.33% of the total WF in the last 30 years. Owing to differences of planting structure, water-conservation technologies, and other factors, the multi-year average WF per unit area of crops was 0.75 × 10⁴ m³ hm⁻² in downstream area, which was higher than that in midstream area (0.57 × 10⁴ m³ hm⁻²) in the last 30 years; therefore agricultural water use efficiency in the downstream area was lower than that in the midstream area, implying that the midstream area has more efficient agricultural water utilization. (2) an initial increase and then decrease of crop WFs in the study area under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios by the end of the century, reaching their peak in 2030s which was higher than that from 1989 to 2020; with the maximum growth rates in the midstream area ranging from −0.85% in SSP5-8.5 to 5.33% in SSP2-4.5 and 29.74% in SSP5-8.5 to 34.71% in SSP2-4.5 in the downstream area. The local agricultural water demand would continue to increase and water scarcity issues would be more severe in the next 10–20 years, affecting downstream areas more. Under the SSP3-7.0 scenario, crop WF values of the midstream and downstream regions will be 2.63 × 10⁸ m³ and 4.22 × 10⁸ m³ in 2030, respectively, which is significantly higher than those of other scenarios and show a long-term growth trend. The growth rate of the midstream and downstream regions will reach 44.71% and 81.12%, respectively, by the end of this century, so the local agricultural water use would be facing more strain if this scenario materializes in the future. Therefore, the Shule River Basin should encourage development of water-saving irrigation technologies, adjust the planting ratio of high water consuming crops, and identify other measures to improve water resource utilization efficiency to cope with future water resource pressures.