Abstract Long-term climate change and short-term extreme weather events both increase the risk of natural disasters, thereby impacting agricultural production. To address these heightened risks, agricultural insurance plays a crucial role in building systemic resilience. By simulating agricultural disasters based on long-term temperature and precipitation trends, this study uses copula functions to characterize the dependence structure linking climate change, agricultural disasters, and agricultural insurance indemnity. Our analysis indicates that the concurrent rise in temperature and decline in precipitation in China (1986–2022), amidst cyclical variations, has been a key driver of the increased drought frequency. Our analysis also reveals that the anomaly of flood- and drought-affected areas in China exhibits a long-term pattern resembling a half-cycle sine wave over the long term. Moreover, with copula functions we identified an upper-tail dependence between agricultural insurance claims and disaster-affected areas. This study informs governments, policymakers, and agricultural insurers on how to promote agricultural insurance as a tool to manage climate risks.
Ye et al. (Fri,) studied this question.