Analysis of variations in 12 extreme temperature indices at 68 meteorological stations on the Yunnan-Guizhou Plateau (YGP) in southwestern China during 1960–2019 revealed widespread significant changes in all temperature indices. The temperature of the hottest days and coldest nights show significantly increasing trends, and the frequencies of the warm days and nights also present similar trends. The temperature of the coldest night has a significant and strong warming trend (0.38 °C/decade), whereas the frequency of frost days shows the fastest decrease (1.5 days/decade). Increases in the summer days are statistically significant, while a decreasing trend for the diurnal temperature range is not significant. Furthermore, there were significant differences in the changes of temperature indices between 1960–1989 and 1990–2019. Most parts of the YGP underwent significant warning, manifesting that the mountainous regions are relatively sensitive and vulnerable to climate change. The correlation coefficients between the temperature indices and various geographical factors (latitude, longitude, and height) reflect the complexity of regional temperature variability and indicate enhanced sensitivity of extreme temperatures to geographical factors on the YGP. It was also found that extreme temperatures generally had weaker correlations with the El Nino-Southern Oscillation, North Pacific Index, Southern Oscillation Index, North Atlantic Oscillation, and East Asian Summer Monsoon Index than with the South Asian summer monsoon index, Nino4 indices and Arctic Oscillation, and there were more insignificant correlations. Regional trends of the extreme temperature indices reflect the non-uniform temperature change over the YGP, which is due to the complex interaction between atmospheric circulation patterns and local topography. The results of this study have important practical significance for mitigating the adverse effects of extreme climatic changes, in particular for the YGP with its typical karst geomorphology and fragile ecological environment.

Light precipitation events are an essential feature of rainfall for agricultural production, risk prediction of drought or flood disasters, and natural resource management in a certain area. We investigated the spatiotemporal variations in light precipitation events with intensities of 0.1–10 mm/day, based on daily precipitation data for the Yellow River Basin (YRB), China, during 1960–2018, and explored their relationships with large-scale atmospheric circulation patterns (LSACPs) and altitude. For further analysis of the changes in the light precipitation events, we classified light rain (0.1 ≤ P < 10.0 mm/day) into five grades of intensity by using equal interval division. Results indicate that the mean annual light precipitation amount and days were 182.7 mm and 80 days, respectively, from 1960 to 2018 over the YRB, accounting for 39.2% and 85.2% of the total annual precipitation amount and days, respectively. Both the amount of light rain and the number of light precipitation days declined by −1.3 mm/decade and −1.4 days/decade, respectively, and suggested that most rain events were of low intensity (0.1 ≤ P < 2.0 mm/day). Light precipitation events mainly occurred in the upper and middle reaches of the YRB and decreased from the southwest to the north. Additionally, changes in light rain events appear to be complex and possibly related to LSACPs and altitude. We found that the LSACPs were a possible mechanism for light precipitation events in the YRB over the past decades. With increasing elevation, light precipitation events decreased significantly throughout the study period. Thus, the decrease in precipitation days mainly occurred at lower altitudes in the YRB. The results also reflect the complexity of regional climate change in the YRB because atmospheric circulation related to climate phenomena not only causes the complex variation in precipitation but also changes its altitude dependence.

Current research on rooftop greening carbon sinks is mostly concentrated at the urban scale or in individual case studies, lacking refined nationwide assessments at the residential-community level. Based on high-precision, building-level rooftop vector data from the CMAB dataset, combined with provincial-level correction factors, this study estimates the carbon sink potential of residential rooftop greening across 31 provincial-level regions in China for 2021. Based on four working hypotheses, the optimal-parameter geographical detector model is applied to identify driving factors. The results show that (1) the potentially available roof area for greening in residential communities is approximately 620,000 ha. The annual carbon sink under the shrub scenario is 49.0% higher than that under the grass scenario, with a corresponding economic value of CNY 8.44 × 108–1.257 × 109; (2) The spatial pattern is characterized by “high in the southeast and low in the northwest” The southeastern half contributes over 96% of the national total carbon sink, while the carbon sink efficiency of the northwestern half is only 1/16 of that in the southeast; (3) The planting pattern exhibits clear regional differentiation, with shrubs being more suitable in the south and grasses in the north, but this conclusion is constrained by management intensity and structural load-bearing capacity; (4) Temperature and precipitation are the dominant natural drivers, and their interaction with economic factors constitutes the strongest explanatory combination. The findings provide refined baseline data for differentiated low-carbon community renewal, and future research should integrate IoT monitoring and eddy covariance measurements to calibrate the NEP coefficients used in this study.

The digital empowerment of agricultural carbon emission reduction plays a crucial role in surmounting emission - reduction bottlenecks and attaining the Dual Carbon goals. This research measured the agricultural carbon emission and the digitalization level in Guizhou Province from 2013 to 2022. An LMDI decomposition model integrating the digitalization level was established to analyze the influencing factors, and a VAR model was employed to forecast the evolving relationships. The results are as follows: (1) Throughout the study period, the agricultural digitalization level demonstrated a gradual upward tendency, while the carbon emission generally presented an “N - shaped” pattern. (2) The influence of digitalization on carbon emission dynamically evolved from “significantly promoting an increase” to “weakly promoting an increase”. The emission - reduction factors (the energy intensity effect and the digitalization - induced energy consumption effect) jointly led to a reduction of 31.68 million tons in emission, whereas the emission - increasing factors (the agricultural economic scale effect and the digitalization - induced production effect) resulted in an increase of 34.83 million tons in emission. (3) Forecasts suggest that the synergistic interaction among these four effects from 2023 to 2030 could achieve a net reduction of 9.25 million tons, facilitating the transformation of emission from an “N - shaped” to an “M - shaped” pattern. The impulse response analysis reveals that for most effects, there are positive driving forces in the first two lags, and all effects transition to inhibitory roles and converge after the third lag. These findings indicate that digitalization alone does not necessarily lead to agricultural carbon emission reduction; its environmental benefits depend on whether digital inputs improve energy efficiency and resource allocation rather than stimulate production expansion. Therefore, Guizhou should prioritize digital applications in precision input management, livestock manure treatment, and clean-energy substitution to promote the coordinated development of agricultural digitalization and carbon reduction.