Spatiotemporal evolution characteristics of agricultural climate resilience in major grain-producing areas of China

Enhancing agricultural climate resilience is essential for ensuring food security and addressing climate change. As China’s major grain-producing areas occupy a central position in national grain production, their agricultural climate resilience has direct implications for national food security and regional agricultural sustainability. Therefore, systematically assessing the level of agricultural climate resilience in these areas and examining its spatiotemporal evolution can provide a scientific basis for improving regional climate adaptation capacity in a differentiated manner and optimizing agricultural policy arrangements. Based on panel data from 180 prefecture-level cities across China’s major grain-producing areas from 2012 to 2023, an evaluation index system is constructed from four dimensions: climate and land productivity, climate adaptability, resource utilization, and green development. Game-theoretic combination weighting, kernel density estimation, spatial Markov chains, the Dagum Gini coefficient, and spatial autocorrelation analysis are employed to investigate the spatiotemporal evolution of agricultural climate resilience. The findings reveal that: 1) Temporally, agricultural climate resilience exhibits a fluctuating upward trend, while absolute differences among prefecture-level cities gradually narrow. Across dimensions, climate and land productivity shows a fluctuating upward trend during the study period, climate adaptability and green development tend to stabilize, and resource utilization follows an upward trajectory. 2) Spatially, prefecture-level cities in Jiangsu, Hubei, and Jiangxi provinces exhibit relatively strong agricultural climate resilience, whereas those in Hebei, Jilin, and Inner Mongolia exhibit relatively low resilience. In terms of spatial distribution, notable regional disparities exist. Low-resilience areas are predominantly concentrated in Inner Mongolia, Jilin, and Hebei, with their number declining, while high-resilience areas are clustered in northern Heilongjiang, coastal Shandong, northern Jiangxi, southern Anhui, and Jiangsu, with their number increasing. In terms of spatial dynamic evolution, agricultural climate resilience at the prefecture level demonstrates spatial interdependence and exhibits a “club convergence” phenomenon. 3) In terms of spatial effects, agricultural climate resilience shows significant positive spatial autocorrelation. Regional disparities in resilience have slightly widened over the study period, yet the absolute differences between regions remain relatively small. This spatial pattern is mainly attributable to two factors. First, spatial disparities in resource endowments and agricultural foundations, such as black soil resources, coastal advantages, and water resource constraints, shape the high-low resilience zoning. Second, pronounced spatial spillover effects enable high-resilience or low-resilience areas to influence neighboring regions, thereby forming “high-high” and “low-low” clustering patterns. Accordingly, several policy implications are derived, including implementing a place-based governance strategy aligned with the spatial pattern of resilience, constructing a regional coordination mechanism to foster positive spatial spillovers, and establishing a risk management framework grounded in dynamic resilience monitoring.