Based on five CMIP6 climate models and using the SSP126 and SSP245 scenarios, this study employed the high−resolution meteorological grid data for China (CN05.1) from 1970 to 2014 (historical period) as the baseline dataset. Delta bias correction, Taylor diagram and Bayesian model averaging (BMA) were used to assess the spatiotemporal changes in agricultural precipitation and heat resources in China from 2015 to 2050. The aim was to provide scientific evidence for optimizing planting systems, adjusting agricultural layout and adapting to climate change. The results showed that: (1) the five CMIP6 climate models and BMA ensemble demonstrate good performance in simulating temperature and precipitation, effectively capturing regional climate characteristics, with better accuracy in temperature simulations. BMA could effectively balance the performance of multiple models in simulating temperature and precipitation. (2) Under the SSP126 and SSP245 scenarios, the average temperature increase rate from 1970 to 2050 was 0.37°C·10y⁻¹ and 0.40°C·10y⁻¹, respectively. With the most significant temperature increasing in the Tibetan plateau, northwest, north and northeast regions, generally exceeding 0.4°C·10y⁻¹. Annual precipitation showed a slight increasing trend, with rates of 5.6mm·10y⁻¹ and 4.8mm·10y⁻¹. Significant increases (≥10mm·10y⁻¹) were observed in south, east and northeast regions, while the southwest region showed a varying degrees of decrease. (3) Compared with the historical period (1970−2014), the regions with an average temperature ≤0°C during 2015−2050 showed significant warming, with the area gradually shrinking. The isotherms for 5°C, 10°C, 15°C and 20°C moved northward by 2.1°, 2.9°, 4.2° and 2.2°, respectively. The region in south China with annual precipitation ≥1500mm slightly expands. (4) Under the SSP245 scenario during From 1970 to 2050, the trend rates of effective accumulated temperature for thresholds ≥0°C, ≥5°C, ≥10°C, and ≥15°C were 10.4°C·d·y⁻¹, 9.3°C·d·y⁻¹, 7.6°C·d·y⁻¹ and 5.9°C·d·y⁻¹, respectively, showing a pattern in which higher temperature thresholds correspond to smaller increases in effective accumulated temperatures. (5) From the distribution of effective accumulated temperature at different thresholds, the low value areas in high−altitude and mid−latitude regions shrinked in the future, while the high value areas in south China expanded to varying degrees.  From 1970 to 2050, the area with an increase in effective accumulated temperature ≥10℃·d·y⁻¹ for thresholds of ≥0℃, ≥5℃, ≥10℃ and ≥15℃ gradually decreased as the temperature threshold rises, while the area with an increase in the range of 0−10℃·d·y⁻¹ continued to expand. Climate change has led to an extended growing season, a northward expansion of planting boundaries and an increase in the cropping index. However, it also presented new challenges for the growth of cool−season crops such as winter wheat, pest control and disease prevention. Strengthening agricultural adaptation strategies to cope with the uncertainties of future climate change is crucial.

Plowing bare farmland is a major source of air pollution in winter and spring in northern China. To clarify the spatial and temporal distribution characteristics and influencing factors of PM_2.5 in Ningxia’s wine grape planting area, and to provide a decision−making basis for air pollution prevention and control, wine grape production units and relevant departments should formulate scientific and reasonable management measures. In this study, the air quality and meteorological monitoring data of 2022 were collected from 9 stations in the grape growing region in the eastern foot of Helan mountain. Spatial interpolation method and correlation analysis methods were used to analyze the spatial and temporal distribution characteristics of PM_2.5 and meteorological influences in this region. The results showed that in 2022, the occurrences of PM_2.5 annual average concentration exceeding 75μg·m⁻³ (level 4) in grape−growing areas such as Bronze gorge ganchengzi meihe winery, Bronze gorge dream shaquan chateau, Bronze gorge west pigeon winery, Helan mountain meihe winery and Pengsheng winery accounted for 13.52%, 10.44%, 6.93%, 6.10% and 6.43% of the occurrences of PM_2.5 concentration at different levels in the whole year, respectively. The annual average concentration of PM_2.5 in non−grape growing region such as Lingwu hedong airport, Dawukou sheyuyuan in Shizuishan and Helan mountain xinxiaolu in Yinchuan exceeded 75μg·m⁻³ (Level 4) accounted for 2.94%, 2.40% and 0.06% of the annual occurrences of different levels of PM_2.5 concentration. The monthly PM_2.5 concentrations at the nine stations were significantly negatively correlated with the average temperature and positively correlated with the relative humidity, without significant test. Only the monthly PM_2.5 concentration of Lingwu hedong airport was significantly negatively correlated with the average wind speed of 2m. It can be seen that the average temperature, a meteorological factor, significantly affects the PM_2.5 concentration, and agronomic operations such as 'spring planting of seedlings and autumn mulching' in the grape base may affect the PM_2.5 concentration by changing the local microenvironment. In order to reduce the release of particulate pollutants, the primary consideration is to reduce the frequency of human disturbance in the grape planting area from January to February, increase the coverage of forest and grass in the grape planting area to reduce the amount of soil wind erosion and the probability of wind erosion, and finally promote the healthy and green sustainable development of the grape planting area.

Determining the straw−to−grain ratio provides a scientific basis for improving the accuracy of straw resource estimation and assessing the carbon sequestration potential of crops. This study reviewed 117 experimental articles related to the maize straw−to−grain ratio and applied Meta−analysis to integrate and quantitatively analyze the straw−to−grain ratio of maize under different regions, years, field management practices and environmental stresses. The results showed that: (1) there was a significant difference in the maize straw−to−grain ratio across China, with the overall trend being higher in the north and lower in the south, with an average value of 1.17. (2) From 2001 to 2022, the maize straw−to−grain ratio decreased by 11.81%, with significant interannual variation. (3) The effect of planting density and nitrogen application rate on the straw−to−grain ratio of maize followed a trend of first decreasing and then increasing. The optimal effect, with the lowest straw−to−grain ratio, was observed at a planting density of 6×10⁴–7×10⁴ plants·ha⁻¹ and nitrogen fertilizer application of 100−200kg·ha⁻¹. Biodegradable plastic film coverage and ploughing with straw return both significantly reduced the maize straw−to−grain ratio, with reductions of 11.29% and 10.48%, respectively. (4) Environmental stress during the flowering period had the greatest impact on the maize straw−to−grain ratio. Combined heat and drought stress had a more significant effect than individual stresses, increasing the straw−to−grain ratio by 75.00%. In conclusion, significant differences in the maize straw−to−grain ratio exist under different production environments. This study identifies a more reasonable maize straw−to−grain ratio under different regions, years, field management practices, and environmental stresses, providing data support for the estimation of maize straw resources and carbon sequestration potential. This is of great significance for improving the utilization efficiency of straw resources and assessing the carbon sink potential of agricultural crops.

Filed experiment of tobacco cultivation was conducted in Dali, Yunnan with five treatments, including no fertilizer (CK), conventional chemical fertilizer (CF), combined organic and chemical fertilizers (CM), organic fertilizer with water−soluble fertilizer (WS) and organic fertilizer with microbial agents (OM). High−frequency monitoring of N₂O flux from both ridges and furrows of tobacco field was conducted during the growth period, using the static chamber−gas chromatography method. Variation in N₂O emissions, emission intensity per unit yield and the influencing factors under different nutrient managements were analyzed, aiming to identify the emission characteristics and provide basic evidences for carbon emission accounting and low−carbon development in tobacco industry. Results showed that cumulative N₂O emissions under different nutrient managements ranged from 3.89 to 5.74 kg·ha⁻¹ during the growth period, with significant differences observed between CF, CM and OM treatments (P<0.05). Compared to CF, other treatments reduced N₂O emissions by 18.45%−32.29%. The N₂O emission factors varied from 0.31% to 1.60%, with the order from highest to lowest being CF, WS, OM and CM. Significant differences were found between CF and CM (P<0.05). N₂O emission intensity per unit yield varied from 1.31 to 2.17 kg N₂O·t⁻¹, with significant reductions of 39.81%, 33.42% and 37.58% in CM, WS and OM, respectively (P<0.05). Cumulative N₂O emissions were positively correlated with total and chemical nitrogen input, negatively correlated with carbon input, organic nitrogen input, and soil mineral nitrogen content, and not significantly correlated with plant nitrogen uptake. In conclusion, organic fertilizer substitution, along with water−soluble fertilizers and functional microbial agents, can effectively decrease N₂O emissions and enhance tobacco yield.

Based on 263 mature corn straw samples, Pearson correlation coefficient (PCC), variance inflation factor (VIF), grey relational analysis (GRA) and recursive feature elimination (RFE) were employed to optimize model input variables, with stem hierarchy−centered trait combinations being identified and optimal machine learning models that integrate agronomic traits being explored for efficient, cost−effective, and rapid non−destructive field estimation of corn straw dry weight. Experimental results identified eight optimal traits through rigorous evaluation of operational feasibility and predictive performance: ear stem diameter, ear height, basal long axis, ear leaf area, cob diameter, cob length, basal short axis and plant height. Machine learning evaluations demonstrated the Extra trees regressor (ETR) achieved superior predictive accuracy (R²=0.92, RMSE=13.52g) compared to eXtreme gradient boosting (XGBoost) implementations. Feature importance and SHAP interpretability analysis revealed stem-related traits as dominant predictors, with basal long axis and ear stem diameter collectively contributing 41.7% of predictive influence. The optimized trait combination offers a reliable, non−destructive approach for estimating corn straw biomass, providing a theoretical foundation and technical framework for the sustainable utilization of corn straw resources. 

Extreme precipitation events occur frequently during maize growing season (late April to early October) in Jilin province, leading to frequent waterlogging disaster that significantly affect maize growth, harvesting and ultimately yields and degraded quality. This study integrated the atmosphere−soil−crop continuum system, utilizing the crop water stress index (CWSDI), vegetation condition index (VCI) and soil moisture condition index (SMCI) to construct a comprehensive waterlogging index (CWI) for maize. Based on the four−factor theory of disaster formation, a comprehensive waterlogging risk assessment model was established to evaluate the integrated risk of maize waterlogging disaster in Jilin province from 1990 to 2022. The results indicated: (1) the CWI during the jointing−silking stage of maize in Jilin province reached as high as 0.98 in 1990−2022, with high−value areas primarily concentrated in the central and southeastern regions of Jilin province. (2) The waterlogging disaster hazards during the maize sowing−jointing, jointing−silking and silking−maturity stage in Jilin province showed an increasing trend from west to east in 1990−2022. Of these, the maize jointing−silking stage had the largest proportion of areas at severe risk, accounting for approximately 28.3% of the total provincial area. (3) The high−risk areas of maize waterlogging disaster in Jilin province were primarily concentrated in the central and southeastern regions from 1990 to 2022, the jointing−silking stage exhibited the largest area of severe risk and above, accounting for approximately 47.9% of the total provincial area. 

To rationally plan the planting distribution of Shatian pomelo in Guangdong, this study employed the ENMeval-optimized Maximum Entropy (MaxEnt) model and ArcGIS software, utilizing 25 geographical distribution records of Shatian pomelo and 27 environmental factors. The aim was to simulate the potential suitable planting areas under historical climatic conditions and future scenarios based on the CMIP6 Shared Socioeconomic Pathways (SSPs), specifically SSP1-2.6 and SSP5-8.5. Additionally, the dominant environmental factors influencing the distribution of Shatian pomelo were analyzed. The simulation was performed using the Maximum Entropy Model (MaxEnt), optimized by the ENMeval package, and analyzed using ArcGIS software. The goal was to identify and analyze the dominant environmental factors influencing the distribution of Shatian pomelo. The results showed that: (1) the MaxEnt model effectively simulated the distribution of suitable cultivation areas for Guangdong Shatian pomelo, with the area under the curve (AUC) reaching 0.912, indicating high model accuracy. (2) During the historical period (1970−2000), low−suitability areas for Guangdong Shatian pomelo covered the largest extent, approximately 105600 km², accounting for 58.7% of the total land area of Guangdong province. These areas were mainly located in the western region of Guangdong, the Pearl river delta and the eastern coastal areas. In contrast, medium− and high−suitability areas, covering about 55900km² (31.1% of the province’s total area), were primarily concentrated in the cities of Meizhou, Heyuan, Shaoguan, Qingyuan and Zhaoqing cities. Among these, Meizhou had the largest highly suitable area, followed by Shaoguan. (3) The dominant environmental factors influencing the suitable cultivation of Shatian pomelo were identified as the precipitation of the wettest month (Bio13), the mean diurnal temperature range (Bio2), the precipitation of the warmest quarter (Bio18) and slope aspect (ASPECT). (4) Under the SSP1−2.6 and SSP5−8.5 scenarios, the total area of medium− and high−suitability zones for Shatian pomelo in Guangdong was projected to expand by 2.5 and 2.3 times, respectively, during the period 2021 to 2040 compared with the historical period. The SSP1−2.6 scenario was expected to produce the largest area of highly suitable zones, covering approximately 57200km², or 31.8% of Guangdong's total land area. Overall, climate change was expected to contribute to the expansion of suitable areas for Shatian pomelo cultivation in Guangdong province. Based on these findings, it is recommended that experimental trials for Shatian pomelo planting be conducted in the western part of Guangdong and the northern cities and counties of the Pearl river delta to assess the comprehensive environmental suitability and explore the feasibility of large−scale cultivation and introduction of Shatian pomelo.

Provincial−level climate risk zoning indicators for the soybean pod borer, which can provide technical support for disaster prevention and mitigation of agricultural pests and diseases, promote the improvement of quality and efficiency in agricultural production, and the high−quality development of meteorological services. This study utilized data on the rate of damage caused by Leguminivora glycinivorella from 25 meteorological stations in Heilongjiang province from 1980 to 2021. Characteristic climatic factors were screened by employing methods such as correlation analysis, path analysis and collinearity analysis. A comprehensive climate risk index for Leguminivora glycinivorella was defined and constructed. The BP artificial neural network method was used to analyze the relationship between the rate of damage and the characteristic climatic factors. Based on the K–means clustering method and typical disaster years, the critical values and grades of comprehensivc climate risk were determined, and the climate risk zoning indicators and models for the occurrence of Leguminivora glycinivorella in Heilongjiang province were established. After verifying the accuracy, climate risk zoning of Leguminivora glycinivorella was conducted for the periods of 1961–1990 and 1991–2020. The results indicated that the five significant characteristic climatic factors that affected the damage rate of Leguminivora glycinivorella in Heilongjiang province were: average temperature of the last ten days of September last year, the monthly average of the daily average temperatures in December of last year, the average of the daily average temperatures in the first ten days of April, the average of the daily average temperatures for May and June and the average daily relative humidity in August. The critical thresholds of the comprehensive climate risk index for the low–risk, medium–risk, high–risk and extremely high–risk zones of Leguminivora glycinivorella in Heilongjiang province were [0, 0.47), [0.47, 0.58), [0.58, 0.68), and [0.68, 1.00], respectively. The rates of damage were [0, 3.5%), [3.5%, 7.0%), [7%, 10%), and [10%, 43%], respectively. The verification results of 40 reserved samples showed that the proportion of samples with fully consistent risk grades calculated by the indicators and the actual risk grades was 67.5% and the proportion of samples with a difference of one grade was 27.5%. This demonstrates that the indicators had indicative significance. Both high–risk regions in 1961–1990 and 1991–2020 were concentrated in the Songnen and Sanjiang plains. The medium and low–risk areas were mainly distributed in the Greater and Lesser Xing'an ranges and the semi–mountainous area of Mudanjiang. Compared with the period of 1961–1990, the period of 1991–2020 saw the emergence of extremely high–risk areas, with the range of high–risk areas was significantly larger than that in 1961–1990, while the range of medium and low–risk areas were decreased significantly.

This research aims to clarify the relationship between the incidence and prevalence of rice blast disease and meteorological conditions for single season rice, and to improve the prediction and forecasting capability of rice blast disease. The phenology data, disease index data and daily meteorological data of single−season rice in Xuancheng city, Anhui province, from 1986 to 2024 were used to identify the main meteorological factors affecting the incidence and the specific date of the critical period for rice blast by analyzing the correlation among the severity levels of rice blast and different meteorological factors across different period. And then according to differential effects of precipitation grades, consecutive rainy days and average temperature on rice blast, the study introduced the rainfall coefficient, the precipitation continuity coefficient and the temperature coefficient, and constructed a comprehensive meteorological index and a rice blast forecasting model based on the meteorological factors by adopting numerical simulation techniques and regression analysis, followed by model validation. The results showed as follows: the primary meteorological factor affecting the incidence of rice blast for single season rice in Xuancheng was the precipitation days, followed by average temperature. The key period for the incidence of rice blast was identified as 6 days before to 19 days after full heading stage. The rainfall coefficient was 0.8, 1.1, 1.7 and 2.2, with corresponding precipitation grades for daily precipitation of light rain, moderate rain, heavy rain and torrential rain and over respectively during critical period of rice blast. The rice blast forecasting model based on the comprehensive meteorological index could effectively integrated the impacts of precipitation days, precipitation grades, continuous rainy days and average temperature on the rice blast in Xuancheng, achieving a modeling accuracy of 73.5% and validation accuracy of 80.0%. The forecasting model established in the study demonstrated practical applicability for rice blast forecasting services for single season rice in Xuancheng.

The striped stem borer (Chilo suppressalis) is a major target for control in Chinese rice production. Its overwintering population directly determines the intensity of infestation in the following year. Based on field survey data and concurrent meteorological data from 2004 to 2022 for the overwintering population of C. suppressalis in Nanchang county of Jiangxi province, this study analyzed the correlation between trends in the overwintering population and pre−winter meteorological conditions. Key meteorological factors were screened to construct multiple linear regression models for both the average and maximum overwintering populations. Standardized regression coefficients (β) were used to evaluate the effects of selected meteorological factors. This study aimed to elucidate the impact of pre−winter meteorological conditions on C. suppressalis and to provide a reference for dynamic pest monitoring under changing climatic conditions. The results indicates that: (1) from 2004 to 2013, the overwintering population of C. suppressalis in Nanchang county exhibited a decreasing trend. After 2014, however, the population increased sharply, with a statistically significant rise (P<0.01). The average overwintering population exceeded 30.00×10⁴ individuals·ha⁻¹, while the maximum overwintering population remained consistently above 130.00×10⁴ individuals·ha⁻¹. Between 2015 and 2022, the occurrence level of C. suppressalis in Nanchang county remained at the highest damage level (Level 5). (2) Key meteorological factors influencing the average overwintering population were the average temperature in mid−October, the minimum temperature in mid−November, and the average relative humidity from late November to early December of the same year, with absolute correlation coefficients all exceeding 0.40 (P<0.05). Similarly, key factors influencing the maximum overwintering population were the minimum temperature in mid−October, the minimum temperature in mid−November, and the sunshine duration in November of the same year, with absolute correlation coefficients all exceeding 0.40 (P<0.05). (3) Multiple linear regression models for the average and maximum overwintering populations were established based on these key factors. Both models fit the training dataset well (average: R²=0.55, P<0.05; maximum: R²=0.56, P<0.01), but their generalization capability was unstable. Analysis using standardized regression coefficients (β) revealed that the most significant pre−winter factor affecting the average overwintering population was the average temperature in mid−October (β=−0.62), showing a significant negative correlation. The main pre−winter factors affecting the maximum overwintering population were the minimum temperature in mid−October (β=−0.40) and the minimum temperature in mid−November (β=0.39), with the former showing a negative correlation and the latter a positive correlation. This study indicates that warming in mid−October before winter suppresses the overwintering base population of C. suppressalis, while higher temperatures in November favor population expansion. This study clarifies the stage−specific and comprehensive impact of pre−winter meteorological conditions on the overwintering population of C. suppressalis and highlights the critical role of pre−winter temperatures in controlling the pest source. It provides a reference for developing dynamic monitoring and control strategies for pests under changing climatic conditions. 

 Based on daily meteorological data from June to September (1971−2023) collected at 33 meteorological stations in Chongqing, this study calculated the crop water deficit index anomaly (CWDIa) on a ten−day scale. Using methods including runs theory, the Mann−Kendall test and wavelet analysis, the spatiotemporal variation characteristics of citrus drought during critical growth stages in Chongqing (June−September) were analyzed combiling the stations of citrus drought occurrence, drought duration, drought intensity and the centroid of citrus drought occurrence. This analysis aimed to provide a scientific reference for citrus drought resistance management in Chongqing. The results showed that: (1) from 1971 to 2023, the ten−day variation of stations of citrus drought occurrence during June−September exhibited an unimodal pattern, characterized by a "slow increase followed by a rapid decrease". The lowest value occurred in early June, peaked in late August, and decreased sharply from early to late September. (2) Interannually, the stations of citrus drought occurrence, the drought intensity and the drought duration during critical growth stages in Chongqing all exhibited non−significant decreasing trends from 1971 to 2023. Periodic variability was observed for all three indicators on time scales of 30y, 12−13y and 4y, with the scale of 30y showing relatively significant variability from 1971 to 2023. (3) From 1971 to 2023, the centroid of citrus drought occurrence during critical growth stages remained in central Chongqing. While compared to other periods in this intensity range, the 1981−1990 and 2021−2023 centroids moving significantly eastward and westward, respectively. (4) Spatially, the most of western Chongqing exhibited high drought frequency, high intensity and long duration during critical growth stages of citrus. In contrast, the southern part of southeastern Chongqing exhibited lower values across all three indicators.

 In order to develop a defense strategy to deal with the impact of lightning disasters on personnel safety and improve the lightning safety assurance capability in rural areas of China, this study, based on the lightning location data and lightning disaster data from 2009 to 2020, used statistical analysis methods to analyze the impact of lightning disasters on personnel safety in rural Chinese Mainland areas from the dimensions of space−time characteristics, casualty environment, etc. The results showed that lightning disasters in Chinese Mainland from 2009 to 2020 were bounded by the Huhuanyong line, with frequent lightning activity in the southeast and relatively few in the northwest. In terms of the impact of lightning disasters on personnel safety, rural areas accounted for about 80%, with a cumulatively impacted 2764 people, but showed an overall decreasing trend. From the perspective of the percentage of lightning disasters affecting personnel safety in rural areas reached its highest at 92% in 2010, and fell to its lowest at 41% in 2020. In terms of spatial dimension, rural areas in the southern region contributed 81.3% to the impact of lightning disasters on personnel safety, forming a radiating distribution pattern of high−risk in the southeast and low loss in the northwest. In terms of time dimension, 95% of lightning disasters that affect personnel safety were mainly concentrated from April to September, which coincided with the "double rush" (busy farming season) season in rural areas, and lightning related casualties were relatively high. In addition, about 40% of personnel safety incidents caused by lightning in rural areas were related to direct participation in agricultural activities, reflecting the large number of outdoor intensive labor work still performed in rural  areas and need to of necessary lightning safety protection measures.

Crop insect pests pose a major biological threat to global food security and agricultural sustainability. In China, insect pests affecting key grain, cotton and oil crops (including rice, wheat, maize, cotton, rapeseed and soybean) were characterized by their high species diversity, wide geographical distribution, severe damage potential and complex control requirements. However, existing research had predominantly focused on single crops or localized monitoring and there was a lack of systematic electronic data resources on crop pests on a national scale. To clarify the current status of pests in China's major grain, cotton and oil crops, this study employed a literature review approach, compiling and integrating pest records from plant protection publications and authoritative works spanning 1979 to 2024. A comprehensive data set of crop pests was created through multiple source data screening, standardization and quality control, encompassing 512 pest species: 196 on rice, 51 on wheat, 54 on maize, 103 on cotton, 39 on canola and 69 on soybeans. The dataset included 16 attributes for each pest species, such as Chinese/common name, Latin scientific name, geographic distribution, host crops, damage symptoms, and control methods, reflecting species diversity, spatial patterns and management strategies. The dataset was stored in excel format (file size: 316 KB) for accessibility and practical use. This electronic resource provided fundamental data for pest monitoring, early warning systems and resistance management analysis. It also supported training of pest image recognition models, optimization of integrated pest management strategies, and policy-making for agricultural sustainability, thereby bridging scientific research and field-level pest control practices. The URL for obtaining the entity dataset was https://doi.org/10.57760/sciencedb.20018.

The relationship between meteorological factors and agricultural production in China was analyzed using statistical methods, based on daily national meteorological data in the summer of 2025. The results showed that the national average temperature in summer (June−August) was 22.4°C, the same as last year, and 1.2°C higher than the average of the same period from 1991 to 2020,representing the maximum value since 1961. The national average precipitation was 325.5mm, 3.6mm above the average of the same period from 1991 to 2020. The national average sunshine hours were 652.0h, 12.8h below the average of the same period from 1991 to 2020. In most of the agricultural region, the sunshine, heat conditions and soil moisture content were suitable, and general meteorological conditions were favorable for the growth and production of grain crops. At the same time, some disasters that occurred in summer had negative impacts on the growth and yield formation of the autumn harvest crops. In the summer−sowing regions of northern China, the phased drought at the onset of summer led to prolonged sowing periods and uneven seedling emergence of summer maize and summer soybean in areas without irrigation facilities, thereby affecting the progress and quality of summer sowing. During the mid−to−late summer (July−August), extensive and persistent high−temperature weather occurred in southern north China, the Huang−Huai−Hai region, the Jiang−Huai region and the Guanzhong plain of Shaanxi province, causing leaf wilting and reduced fruiting rates in autumn−harvested crops such as maize, soybean, cotton, and peanut, consequently impacting yield formation. In addition, persistent high temperatures during midsummer in the Yangtze river basin had affected the growth and development of single−cropping rice and economic forest fruits in certain areas.