This study aimed to investigate the impact of low-temperature (LT) stress during the jointing stage on three most representative wheat varieties in the Huang-Huai-Hai region: “Shannong 38” (a robust winter wheat variety), “Jimai 22” (a semi-winter wheat variety), and “Zhenmai 12” (a weak winter wheat variety). The objective was to explain the sensitivity and change thresholds of various physiological and yield indicators of three winter wheat varieties to low temperatures during the jointing stage and to construct an index for the grading of LT disasters during the jointing stage using principal component analysis. Controlled environment experiments were conducted using an artificial climate chamber during the jointing stage of winter wheat. Five daily minimum temperature treatments were applied, namely (T1, −6 °C); (T2, −3 °C); (T3, 0 °C); (T4, 3 °C); (T5, 6 °C); and control (CK, 8 °C). The duration of treatments was divided into three levels: (D1: 2 days), (D2: 4 days), and (D3: 6 days). It was found that the photosynthetic parameters and chlorophyll content showed a decreasing trend with the increase in the degree of LT stress. The activities of protective enzymes and endogenous hormones increased during the early stages of LT stress or at relatively high temperatures. However, they decreased significantly with an increase in LT stress. Among the varieties, “Zhenmai 12” exhibited a yield reduction rate exceeding 10% under 3 °C LT stress for more than 4 days and a yield reduction rate exceeding 20% under 0 °C LT stress for more than 6 days. “Jimai 22” showed a yield reduction rate exceeding 10% under 3 °C LT stress lasting more than 2 days, and a yield reduction rate exceeding 20% under −3 °C LT stress lasting more than 4 days. “Shannong 38” experienced a yield reduction rate exceeding 10% under 0 °C LT stress lasting more than 4 days and a yield reduction rate exceeding 20% under −6 °C LT stress lasting more than 6 days. Principal component analysis (PCA) conducted on all trait indicators of the three winter wheat varieties revealed that “Zhenmai 12” experienced mild LT stress at 6 °C for 2 days, moderate LT stress at 0 °C for 6 days, and severe LT stress at −3 °C for 6 days. “Jimai 22” experienced mild LT stress under 6 °C for 6 days, moderate LT stress under 0 °C for 4 days, and severe LT stress under −6 °C for 2 days. “Shannong 38” experienced mild LT stress under 3 °C for 4 days, moderate LT stress under 0 °C for 4 days, and severe LT stress under −6 °C for 6 days.

Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges such as data accessibility and soil heterogeneous necessitate the use of numerical simulations for their effective regional-scale applications. The existing simulation methods like physical process models and machine learning (ML) algorithms have limitations: physical models struggle with parameter acquisition at regional scales, while ML algorithms face difficulties in agricultural settings due to the presence of crops. As a more advanced and complex branch of ML, deep learning algorithms face even greater limitations related to crop growth and agricultural management. To address these challenges, this study proposed a novel hybrid monitoring system that merged ML algorithms with a physical process model. Initially, we employed the Random Forest (RF) regression model that integrated multi-source environmental factors to estimate soil moisture prior to the sowing of winter wheat, achieving an average coefficient of determination (R²) of 0.8618, root mean square error (RMSE) of 0.0182 m³ m⁻³, and mean absolute error (MAE) of 0.0148 m³ m⁻³ across eight soil depths. The RF regression models provided vital parameters for the operation of the Water Balance model for Winter Wheat (WBWW) at a regional scale, enabling effective drought monitoring and assessments combined with the Soil Moisture Anomaly Percentage Index (SMAPI). Subsequent comparative analyses between the monitoring system-generated results and the actual disaster records during two regional-scale drought events highlighted its efficacy. Finally, we utilized this monitoring system to examine the spatiotemporal variations in drought patterns in the HHH region over the past two decades. The findings revealed an overall intensification of drought conditions in winter wheat, with a decline in average SMAPI at a rate of −0.021% per year. Concurrently, there has been a significant shift in drought patterns, characterized by an increase in both the frequency and extremity of drought events, while the duration and intensity of individual drought events have decreased in a majority of the HHH region. Additionally, we identified the northeastern, western, and southern areas of HHH as areas requiring concentrated attention and targeted intervention strategies. These efforts signify a notable application of multi-source data fusion techniques and the integration of physical process models within a big data context, thereby facilitating effective drought prevention, agricultural management, and mitigation strategies.

Along with climate warming, extreme heat events have become more frequent, severe, and seriously threaten rice production. Precisely evaluating rice heat levels based on heat duration and a cumulative intensity index dominated by temperature and humidity is of great merit to effectively assess regional heat risk and minimize the deleterious impact of rice heat along the middle and lower reaches of the Yangtze River (MLRYR). This study quantified the response mechanism of daytime heat accumulation, night-time temperature, and relative humidity to disaster-causing intensity in three categories of single-season rice heat (dry, medium, and wet conditions) using Fisher discriminant analysis to obtain the Heat Comprehensive Intensity Index daily (HCIId). It is indicated that relative humidity exhibited a negative contribution under dry heat, i.e., heat disaster-causing intensity increased with decreasing relative humidity, with the opposite being true for medium and wet heat. The Kappa coefficient, combined with heat duration and cumulative HCIId, was implemented to determine classification thresholds for different disaster levels (mild, moderate, and severe) to construct heat evaluation levels. Afterwards, spatiotemporal changes in heat risk for single-season rice through the periods of 1986–2005, 2046–2065 and 2080–2099 under SSP2-4.5 and SSP5-8.5 were evaluated using climate scenario datasets and heat evaluation levels carefully constructed. Regional risk projection explicitly revealed that future risk would reach its maximum at booting and flowering, followed by the tillering stage, and its minimum at filling. The future heat risk for single-season rice significantly increased under SSP5-8.5 than SSP2-4.5 in MLRYR. The higher risk would be highlighted in eastern Hubei, eastern Hunan, most of Jiangxi, and northern Anhui. As time goes on, the heat risk for single-season rice in eastern Jiangsu and southern Zhejiang will progressively shift from low to mid-high by the end of the twenty-first century. Understanding the potential risk of heat exposure at different growth stages can help decision-makers guide the implementation of targeted measures to address climate change. The proposed methodology also provides the possibility of assessing other crops exposure to heat stress or other extreme events.

Climate change has been highlighted over the last decades worldwide, with pronounced higher warming trends for China. Induced by climate change, to some extent, agricultural production has changed, as well as the climatic resources during the agricultural growing season. An obvious longer potential agricultural growing season (PAGS) was detected in the latest decades from 1961–2020. The spatial–temporal characteristics of change and variation of climatic resources during the PAGS were explored, utilizing comparisons of the differences in mean magnitude, standard deviation, and trends. In the period of 1991–2020 relative to 1961–1990, alterations in PAGS were characterized by increases of 0–1.5 °C in mean air temperature overall, 0.8–23.8% increases in precipitation in the southeast, northeast, and west, as well as a decrease of 2.1–10.2% in insolation in central-south regions but an increase of 0.3–6.7% in the north and west. The features were pronounced during the PAGS in the primary agricultural zones as follows: (1) Northeast China, increasing and stable temperature but unstable precipitation and insolation; (2) North, eastern-west, and Southwest China, increasing but unstable temperature, decreasing but stable precipitation as well as decreasing and unstable insolation; (3) Southeast China, increasing but unstable temperature and precipitation as well as decreasing and unstable insolation; (4) West China, increasing but unstable temperature and precipitation as well as increasing but unstable insolation. Further comparisons between agro-climatic change and climate change indicated that temperature alterations during the PAGS were less numerous while precipitation and insolation were more unstable than the changes during the whole year. These findings can assist the understanding of regional agricultural climate changes and guide agricultural production practices in response.