This paper explores the optimal temperature change in normalized difference vegetation index (NDVI) growth in the Huang-Huai-Hai Plain under the background of climate change, aiming to better cope with the impact of global warming on vegetation growth. The temporal and spatial variation characteristics of the NDVI and temperature factors were analyzed by using the NDVI, average temperature (Tavg), maximum temperature (Tmax) and minimum temperature (Tmin) datasets from 1982 to 2020. Through the Buishand U test and sliding slope detection, 1998 was determined to be a year of abrupt climate change. Furthermore, SHAP important feature analysis, a generalized additive model, correlation analysis, and other methods were used to identify the trend in the optimum temperature of vegetation growth before and after climate change. The results showed that (1) from 1982 to 2020, Tavg, Tmax, Tmin, and the NDVI in Huang-Huai-Hai Plain showed a significant upward trend. At the same time, the spatial distribution of these indicators shows the distribution characteristics of high in the south and low in the north. (2) The NDVI was positively correlated with Tavg, Tmax, and Tmin, and the correlation with Tmin was most significant. (3) The most suitable Tavg, Tmax, and Tmin intervals for vegetation growth were 20~30 °C, 25~35 °C, and 16~25 °C, respectively. (4) The optimum temperature range of vegetation growth was expanded after climate change, and the change rates of the Tavg and Tmax lower limits reached 24% and 25%, respectively, under the best condition of vegetation growth. (5) After abrupt climate change, the temperature suitable for vegetation growth increased significantly in the northern part of the plain but decreased slightly in the southern part.

Global climate change increasingly impacts agroecosystems, particularly through high-temperature–drought and low-temperature–drought compound events. This study uses ground meteorological and remote sensing data and employs geostatistics, random forest models, and copula methods to analyze the spatial and temporal distribution of these events and their impact on winter wheat in the Huang-Huai-Hai Plain from 1982 to 2020. High-temperature–drought events increased in frequency and expanded from north to south, with about 40% of observation stations recording such events from 2001 to 2020. In contrast, low-temperature–drought events decreased in frequency, affecting up to 80% of stations, but with lower frequency than high-temperature–drought events. Sensitivity analyses show winter wheat is most responsive to maximum and minimum temperature changes, with significant correlations to drought and temperature extremes. Copula analysis indicates temperature extremes and drought severity are crucial in determining compound event probability and return periods. High-temperature–drought events are likely under high temperatures and mild drought, while low-temperature–drought events are more common under low temperatures and mild drought. These findings highlight the need for effective agricultural adaptation strategies to mitigate future climate change impacts.