Abstract: Identifying possible connections between heat stress and large-scale circulation indices (LSCI) is beneficial for mitigating the impacts of climate anomalies on summer maize cultivation. Based on daily maximum tempe-rature data and summer maize yield records from 1980 to 2019 in Hebei, Henan, Shandong, Anhui, and Jiangsu, we evaluated the applicability of nine types of extreme temperature indices on describing heat stress during growth period through first-order difference processing, correlation analysis and regression analysis. We identified the spatio-temporal modes of key disaster-causing factors through principal component analysis, and further analyzed their responses to the circulation indices using time-lag correlation. The results showed that the spatiotemporal characte-ristics of heat stress could be better evaluated with the maximum cumulative heat degree-day (MHDD) of high-temperature spell as a key disaster-causing factor when the high temperature threshold was set as 32 ℃. There was a significant spatial distribution pattern of disaster intensity of heat stress, decreasing from south to north. The strengthening trend of heat stress in the central study area was more significant than other sub-regions. The multi-linear model based on previous LSCI data could simulate heat stress. The Pacific warm pool index was the primary precursor signal of heat stress in multiple sub-regions.

Key words: heat stress, large-scale circulation index, summer maize, principal component analysis