Based on the panel data of daily meteorological stations and winter wheat yield in Henan Province from 2000 to 2023, this study comprehensively used the Mann–Kendall trend test, wavelet coherence analysis (WTC), and other methods to reveal the temporal and spatial evolution of extreme precipitation and its multi-scale stress mechanism on grain yield. The results showed the following: (1) Extreme precipitation showed the characteristics of ‘frequent fluctuation-gentle trend-strong spatial heterogeneity’, and the maximum daily precipitation in spring (RX1DAY) showed a significant uplift. The increase in rainstorm events (R95p/R99p) in the southern region during the summer is particularly prominent; at the same time, the number of consecutive drought days (CDDs > 15 d) in the middle of autumn was significantly prolonged. It was also found that 2010 is a significant mutation node. Since then, the synergistic effect of ‘increasing drought days–increasing rainstorm frequency’ has begun to appear, and the short-period coherence of super-strong precipitation (R99p) has risen to more than 0.8. (2) The spatial pattern of winter wheat in Henan is characterized by the three-level differentiation of ‘stable core area, sensitive transition zone and shrinking suburban area’, and the stability of winter wheat has improved but there are still local risks. (3) There is a multi-scale stress mechanism of extreme precipitation on winter wheat yield. The long-period (4–8 years) drought and flood events drive the system risk through a 1–2-year lag effect (short-period (0.5–2 years) medium rainstorm intensity directly impacted the production system). This study proposes a ‘sub-scale governance’ strategy, using a 1–2-year lag window to establish a rainstorm warning mechanism, and optimizing drainage facilities for high-risk areas of floods in the south to improve the climate resilience of the agricultural system against the background of climate change.

Air pollution remains a major global environmental health risk, and clarifying how the Air Quality Index (AQI) evolves and responds to coupled meteorological and human drivers is essential for effective governance, especially in high-altitude and ecologically sensitive regions such as Qinghai Province. Here we propose and apply “a multi-evidence framework for interpreting regional air quality dynamics in Qinghai Province”, integrating spatiotemporal characterization, Morlet wavelet–based periodicity diagnosis, machine-learning–based machine-learning prediction, and interpretability-based mechanism inference, together with CMIP6-SSP scenario projection. The results show: (1) Qinghai’s AQI exhibits pronounced spatial heterogeneity during 2014–2023, with persistently higher levels in the eastern urban corridor, lower levels in the southern plateau, and marked interannual variability in the Haixi region. (2) Wavelet analysis confirms a dominant annual-scale periodicity for AQI and major pollutants, indicating strong seasonal forcing with episodic strengthening in some years. (3) Among multiple candidate models, the best-performing boosting model provides robust prediction and serves as the basis for interpretation. (4) By jointly using SHAP attribution and interaction interpretation with two-dimensional partial dependence analysis, we identify temperature as the primary driver, with wind speed and precipitation acting mainly as conditional regulators; the responses are distinctly nonlinear and reveal clear “high-risk windows” under specific joint meteorological ranges. (5) SSP-based projections suggest that spatial hotspots are likely to persist; particulate matter and primary gaseous pollutants tend to decline or stabilize under mitigation-oriented pathways, while ozone increases emerge as an increasingly important constraint, particularly under higher-forcing scenarios. These findings support climate-sensitive, region-targeted strategies combining clean-energy transition, coordinated multi-pollutant control with an ozone-oriented focus, and sustained management of persistent hotspot regions.

Enhancing herders’ livelihoods is essential in balancing human–land interactions and promoting inclusive, sustainable development within protected area management. Using a household survey (N = 3539; March–June 2025) and a mixed-methods quantitative approach (weighted TOPSIS, obstacle degree, Spatial Durbin Model, and hierarchical regression), we assessed household livelihood resilience in the Lancang River source area of Sanjiangyuan National Park. Key findings included the following. Overall livelihood resilience was moderate, with a mean score of 0.411. This was characterized by a marked weakness in learning capacity (0.358) and relative strength in self-organization (0.431). Major barriers to resilience included cooperative participation (obstacle degree: 8.14%), education levels (7.58%), skills training (7.18%), household savings (6.40%), and information acquisition abilities (5.97%). The spatial analysis revealed a core-periphery pattern of resilience, evidenced by significant negative spatial autocorrelation (W×HLR coefficient = −0.787, p = 0.001), suggesting competitive interactions among villages. Within this pattern, cooperative participation induced significant positive spatial spillovers (W×X₈ coefficient = 0.147, p < 0.001), while benefits derived from information acquisition abilities remained localized (Direct Effect = 0.061, p < 0.001). The pathways to resilience were associated with household heterogeneity. Associations between key factors and resilience varied across demographic groups, with women and youth benefiting more from skills training and education. Livelihood strategies were linked to information utilization, with cordyceps-dependent households exhibiting greater sensitivity to information acquisition abilities (interaction coefficient = 0.009, p = 0.009). The institutional environment shaped organizational benefits; the positive association with cooperative participation diminished in the core protected zone (interaction coefficient = −0.011, p = 0.036). These findings highlight household heterogeneity as a key factor influencing diverse resilience pathways. They also emphasize the need for targeted, spatially specific, and group-oriented governance strategies.