A method was put forward to identify the combined heat and drought (CHD) events that occurred in summer and affected spring maize in Liaoning province. The spatial and temporal characteristics of CHD and its effects on maize were evaluated based on daily meteorological data at 52 meteorological stations in Liaoning from 1961 to 2020, as well as agricultural data including details of the maize development periods. The effects of CHD on the photosynthetic capacity of maize were evaluated using SIF remote sensing data from 2001 to 2020. The differences in maize photosynthetic capacity in the summers of 2009 and 2018 were compared in detail. The results show that from 1961 to 2020, the occurrence range, frequency, and severity of summer CHD events increased in Liaoning. CHD events were more frequent in June/July, and higher-intensity CHD events were more frequent in July/August. From 1961 to 2020, CHD events occurred in 69% of the years of reduced meteorological yield, and reduced meteorological yield occurred in 41% of the years with CHD events. Maize solar-induced chlorophyll fluorescence (SIF), an index of photosynthesis, was sensitive to temperature (negatively correlated) and precipitation (positively correlated). The CHD events slowed the increasing SIF from the three-leaf stage to the jointing stage, and they stopped the increasing SIF or decreased it at the tasseling–flowering to silking stages. Therefore, maize photosynthesis may be most sensitive to CHD during the flowering to silking stages, and CHD during the silking to milk stages may have the greatest impact on maize yield. Understanding the effects of CHD on maize growth/yield provides a scientific basis for reducing its negative impacts on maize production.

Northeast China (NEC) is a region sensitive to climate change. However, the adoption of long-season maize cultivars in NEC has caused a substantial yield increase under climate change conditions. It is important to determine whether such cultivar adoptions are effective throughout the whole NEC to sustainably increase grain yield. In this study, phenological observations and meteorological data at six sites from 1981 to 2018 were used to detect thermal time (TT) trends during the maize growing period. TT, as a parameter for measuring changes in maize cultivars, was used in the crop simulation model CERES-Maize to examine the variations in maize yield produced with different cultivar × climate combinations in different decades. In NEC, both TTs from emergence to anthesis and from anthesis to physiological maturity showed significant increasing trends from 1981 to 2018. Simulation results for humid areas revealed that adopting longer-season cultivars during 2000–2018 caused yield increases, ranging from 6.3% to 13.3%, compared with the 1980s. However, for stations in semi-humid areas, maize grain yield showed a decrease or a small increase (from −12.7% to 8.0%) when longer-season cultivars were adopted during 2000–2018. For semi-humid areas, decreasing trends in the ratios of rainfed yield to no water-stress yield (Y_rainfed/Y_{no water-stress}) and lower Y_rainfed/Y_{no water-stress} values during 2000–2018 indicated a growing sensitivity of maize production to water, which was attributed to changes in TT and precipitation. Our results indicate that, for the semi-humid area, maize yield was limited by water after introducing cultivars with higher TT requirement under climate change conditions. Therefore, securing food supplies will depend on increases in water-use efficiency levels and other adaptive strategies, such as varietal diversification, drought-resistant varieties, conservation tillage and irrigation.

Accurate feature identification of drought disaster events is required for proper risk management in agriculture. This study improved the crop water deficit index (CWDI) by including the daily meteorological, crop development stage, soil moisture content, and yield data for 1981–2020 in northeastern China. Two drought characteristic variables (drought duration and intensity) were extracted using the theory of runs to produce the improved crop water deficit index (CWDIwp). Thresholds for the bivariate indicators were also determined for agricultural drought events of varying severity. A joint distribution model for drought variables was constructed based on five types of Archimedean copulas. The joint probability and the joint recurrence period for agricultural drought events were analyzed for drought events with varying intensities in northeast China. The results suggest that the CWDIwp can reliably identify the onset, duration, and intensity of drought events over the study area and can be used to monitor agricultural drought events. The conditional probability of drought intensity (duration) decreased as the drought duration (intensity) threshold increased, whereas the drought recurrence period increased as the threshold for drought duration and intensity rose. In the period (1981–2020), drought intensity in the three Northeastern provinces showed an increasing trend in the order Jilin Province > Liaoning Province > Heilongjiang Province. The spatial distribution of the joint probability and the joint recurrence period was obvious, and the joint probability showed a decreasing distribution trend from west to east. The distribution trend for the joint probability was opposite to that of the joint recurrence period. Furthermore, the areas with high drought probability values corresponded to the areas with low values for the recurrence period, indicating that the drought occurrence probability was higher, and the recurrence period value was lower in the drought-prone areas. The high-risk drought areas (60–87%) were in western Liaoning and western Jilin, with a recurrence period of 1–3 years, whereas the low-risk areas ( moderate drought > severe drought > extreme drought. The order for the recurrence period was light drought < moderate drought < severe drought < extreme drought. The results provide technical support for disaster prevention and mitigation in drought risk management.

The Northeast China maize belt is one of the three major golden maize belts in the world and has been severely affected by climate change, however, the evapotranspiration (ET) partitioning is not clear. It is important to study ET and its components under climate change. In this paper, the water balance model SOILWAT2 was used to estimate ET partitioning in drought and humid years, seasons, and maize growth stages from 1989 to 2018 over rainfed maize farmland. The results indicated that the SOILWAT2 model performed well for the prediction of ET and its partitioning compared with eddy covariance measurements. The mean yearly ET, transpiration (T), soil evaporation (Es), and canopy interception evaporation (Int) were 432.3 mm, 197.6 mm, 204.7 mm and 19.2 mm, respectively, over 30 years. Es/ET was 6.3% lower in drought years than in humid years, T/ET was conversely higher (6.2% higher in drought years). There was no clear difference of Int/ET between humid and drought years. In the growing season, T/ET, Es/ET, and Int/ET varied from 40.0% to 75.0%, 22.8% to 55.7%, and 0.7% to 7.0%, respectively. T/ET decreased along with the growth of maize and was greatest at the greening–jointing stage. Es/ET was smallest at the greening–jointing stage. We found a power function relationship between T/ET, Es/ET, and leaf area index (LAI) and above-ground biomass. Our results indicated that for the rainfed farmland, drought may limit maize yield by increasing water loss of maize through increasing T under climate change conditions. Therefore, securing food yield will depend on increases in water-use efficiency and other adaptive strategies, such as drought-resistant varieties, and irrigation.

Co-occurring extreme heat, drought, and moisture events are increasing under global warming and pose serious threats to ecosystem and food security. However, how to effectively link compound agrometeorological disasters (CADs) with climate change has not been well assessed. In this study, we focus on the comprehensive influence of large-scale climate factors on CADs rather than extreme meteorological elements. The results indicate that there are two main CADs of spring maize in Shenyang, Northeast China (NEC), including concurrent drought and cold damage (DC) and drought in multiple growth periods (MD). The related circulation anomalies at mid–high latitudes are identified as four patterns, namely, the Northeast Asia Low (NEAL) and Ural High (UH) patterns affecting DC, the Baikal High and Okhotsk Low (BHOL), and the Northeast Asia High (NEAH) patterns leading to MD. The vertical profile and water vapor transport anomalies further demonstrate the influence mechanism of large-scale circulation on compound heat–moisture stresses. This study highlights the role of atmospheric circulation, which can provide effective predictors for these synergistic agrometeorological disasters.