With the development of science and technology and social economy, the connotation of climate resources has changed significantly. Deepening the understanding of climate resources was an urgent need to promote agricultural development. Authors reviewed the conceptual evolution of agricultural climate resources, their connotations and characteristics, and summarize the new situation of agricultural climate resources and their utilization in the context of climate change. The results showed that: (1) agricultural climate resources were the climate resources and climate environment that could be used by agriculture in the climate system, including the dynamic changes of material, energy, information and other forms and their space−time. They were an essential part of the natural resources of agricultural. (2) As a part of climate resource, the common characteristics of agricultural climate resources included: infinite cycle of renewable and limited in a certain space−time range, borderless wide distribution and regional imbalance, continuity, periodicity and unstable fluctuation interdependent constraints and irreplaceability, nonsubstance, resource value of multiple suitability and relativity. Unlike other types of nonlinear resources, it was characterized by nonlinearity, correlation, synergy and potential value. (3) Climate change had brought new trends to China's agricultural climatic resources and active adaptation to climate change was the basic principle that should be adhered to in the development and utilization of China's agricultural climatic resources at present. The main methods were: dynamic and moderate adjustment, taking care to leave room. The three diversities of agricultural biology, climate and agricultural activities, were coordinated. Taking full advantage of modern technology focused on the development and utilization of agricultural climate resources in the form of information. Based on agricultural climate resource surveys and refined zoning, regional climate response technology systems for crop cultivation and animal health feeding should be constructed. 

Crop yields and quality are the result of a combination effect of multiple factors such as variety, meteorology, soil and technical measures. In particular varieties, soil and technical conditions, the yield and quality of crops are mainly determined by the degree to which meteorological conditions are met. Establishing a physical link between meteorological conditions and yield and quality is important for guiding crop production. Based on the definition of crop meteorological quality, this paper explored the formation principle of crop meteorological quality, proposes evaluation methods to evaluate crop meteorological quality, and conduct case studies. The results showed that the meteorological quality of crops referred to the extent to which meteorological conditions (a combination of one or more meteorological elements) meet the production requirements of crops, and it was a comprehensive reflection of the yield level or quality characteristics of crops. Meteorological quality requirements for different crops and different yields and qualities were varied. The yields and qualities of crops formed under different meteorological qualities were also different. People could regulate the meteorological quality of crops within a certain range through technical measures. Crop meteorological quality had multi−dimensional characteristics such as temporal dynamics, spatial heterogeneity, relative stability and volatility and human controllability. The meteorological quality could be evaluated by constructing a crop meteorological quality index to comprehensively assess the degree to which meteorological conditions meet crop production. Taking the spring tea of Tieguanyin in Anxi, Fujian province as an example, the meteorological quality index for spring tea was constructed. Tea meteorological quality evaluation was carried out in typical years of 2011, 2012, 2019 and 2020, the average meteorological quality index of the 12 sampling points were 1.59, 1.68, 2.60 and 2.68 respectively. They respectively belong to the grades of well, well, excellent and excellent. The results were consistent with the previous annual survey on the meteorological quality of tea. This research is instructive for deepening the understanding of meteorological conditions and promoting agricultural production increase, quality improvement and efficiency enhancement. 

Accurate estimation of reference evapotranspiration (ET₀) is crucial for optimizing agricultural irrigation and managing water resources. However, the application of traditional empirical models is often limited due to missing meteorological data. This study investigated the applicability of a Genetic algorithm−optimized backpropagation neural network (GA−BP) model for ET₀ simulation in the Three Gorges reservoir area. Using daily meteorological data from six observation stations between 1980 and 2023, 16 kinds of GA−BP−based ET₀ estimation models were developed and compared with three traditional empirical models: Hargreaves−Samani (H−S), Makkink and Irmak−Allen (I−A). The results showed that the GA−BP models significantly outperform traditional models across different regions. When only temperature and solar radiation data were used, the GA−BP2 model improved R² by 15.69%, reduced MAE by 35.36%, and decreased RMSE by 40.63% compared to the H−S model. With the addition of sunshine duration, the GA−BP6 model improved R² by 31.20%, reduced MAE by 27.33% and lowered RMSE by 23.47% compared to the Makkink model. After incorporating relative humidity, the GA−BP12 model outperformed the I−A model, with a 15.10% increase in R², a 57.88% reduction in MAE, and a 43.92% decrease in RMSE. Therefore, the GA−BP model is recommended for ET₀ estimation in the Three Gorges reservoir area, especially under conditions of limited meteorological data.

Accurate prediction of the initial flowering period (IFP) of lavender is essential for optimizing field management and enhancing tourism services in Yining of Xinjiang. Based on the observed data of IFP from 2001 to 2024 at the Yining horticultural station and the meteorological data from February to May of the same period, the correlations between the IFP and meteorological factors were analyzed. Building on the biological characteristics of lavender, pre−flowering climatological year types were classified via fuzzy clustering, and meteorological pathways were analyzed using path analysis to identify key predictors. Subsequently, multiple linear regression models were developed and validated. The results showed that: (1) significant correlations were observed between IFP and meteorological factors such as the average temperatures in late February, early March, and mid−March, precipitation in early February, and accumulated growing degree−days (GDD0 ) from the start of spring to May 31. GDD0 had the greatest influence, surpassing both precipitation and decadal scale variables. This dominance of GDD0 as a predictor was consistent and statistically significant (P<0.01) across all year−type classifications. (2) The year-type-classified model demonstrated superior accuracy (ME=0.5d, RMSE=0.8d, RE=0.6%) compared to the non-classified model (ME=0.9d, RMSE=1.2d, RE=0.9%). The implementation of the year-type classification model is recommended for operational phenological forecasting. Therefore, it is recommended to adopt the year-type classification–based model for operational forecasting of the IFP of lavender onset in Yining.

Based on statistical data and field survey data from the main apple producing region of Shaanxi province from 1990 to 2022, this study employed the Gini coefficient, center of gravity migration and spatial autocorrelation analysis to investigate the spatio−temporal evolution of apple production layout. Additionally, boosted regression trees was applied to explore the influencing factors of total production changes, providing a basis for optimizing apple industry configuration and promoting high−quality industry development. The results showed that: (1) during the study period, the development of apples in the main producing region underwent four stages of evolution: boom, adjustment downturn, transition, stabilization and promotion. The trajectory of production center migration showed a pattern of first moving southward and then northward. The Gini coefficient decreased from 0.77 to 0.71, indicating a slight reduction in industrial agglomeration. In 2022, the total production in the northern Weibei accounted for 54.47% of the province's total, maintaining its core production area status. (2) The Moran's I for apple planting area first increased to 0.42 in 2010 and then decreased to 0.29 in 2022, indicating adjustments in the spatial layout structure of apple production. High agglomeration were concentrated in the northern Weibei region and expanding northward, while low agglomeration were distributed in the northern mountainous regions and the western Weibei dry plateau. (3) Changes in total production were influenced by meteorological enviroment, technological progress, market economy, social development, conditions of agricultural production and policy environment. Among these, production per hectare (38.85%), gross output value of agriculture, forestry, animal husbandry and fishery (18.36%), comparative income (17.91%), and total agricultural inputs (10.52%) were the top four key factors influencing the extent of change.

To precisely characterize the spatial distribution of climate suitability for pasture cultivation in the Chongqing section of the Three Gorges reservoir area (TGRA), a comprehensive assessment was conducted. Daily meteorological data from 1058 observation stations from 2011 to 2020, a 30−meter digital elevation model (DEM), and climatic suitability indicators for five commonly cultivated pasture species were integrated. Eight factors, including mean annual temperature, accumulated temperature above 0℃, extreme temperature, mean annual precipitation, sunshine duration, frost−free period, mean relative humidity and elevation, were selected as zoning indicators. These indicators were spatialized using interpolation methods, and a new Pasture climate suitability index (PCSI) was constructed based on fuzzy mathematics and a weighted comprehensive evaluation method. Natural breaks (Jenks optimization) were used to classify climate suitability into three levels: suitable, sub−suitable and unsuitable, while areas categorized as other refer to water bodies and built−up land excluded from the suitability evaluation. The results showed that the PCSI values across the TGRA ranged from 0.47 to 0.76, with suitable areas (0.72−0.76) and sub−suitable areas (0.66−0.72) accounting for 65.0% and 32.2% of the total area, respectively, indicating favorable climatic conditions for pasture growth in most of the region. All five pasture species exhibited a relatively high level of climatic adaptability. In terms of overall suitability, the ranking from highest to lowest was: alfalfa > hybrid giant napier > sweet sorghum > annual ryegrass > napier grass. Although the spatial suitability patterns of the five species were generally consistent, there were regional differences in the proportions of suitability classes. Suitable and sub−suitable zones were widely distributed, while unsuitable areas were mainly located in the northern, eastern, southwestern and some southern high−altitude regions of the TGRA. The largest Suitable area proportion was observed for alfalfa (57.9%), while the highest proportion of unsuitable areas occurred for annual ryegrass (18.0%) and the lowest for alfalfa (6.7%). 

To address the challenges posed by low temperature and continuous rainy weather disasters affecting flue−cured tobacco production in Hubei province, this study established a dynamic monitoring index system for such disasters across the root extension stage, vigorous growth stage and maturation stage of flue−cured tobacco. The system was developed using historical disaster and meteorological observations data from 1961 to 2020, high−resolution grid−based analysis data from 2013 to 2022, and climate projection data from the BCC−CSM model for the period 2023−2052. A risk assessment model for low temperature and continuous rainy weather disasters in Hubei’s flue−cured tobacco was constructed based on three dimensions: the hazard of disaster−inducing factors, the sensitivity of the disaster−prone environment and the vulnerability of the exposed body. The risks under future climate scenarios were evaluated by integrating the low−forcing SSP1−2.6 and medium−forcing SSP2−4.5 emission scenarios. The results indicated that: (1) the dynamic monitoring index system for low temperature and continuous rainy weather disasters in Hubei’s flue−cured tobacco included daily average temperature, sunshine hours, precipitation and the duration of the weather process. Threshold values for key indicators were defined based on the climatic resources requirements during different growth stages. For example, during the root extension stage, a precipitation event lasting≥4d (allowing 1d without precipitation) or ≥7d (allowing 2 non−adjacent days without precipitation), with a daily average temperature ≤17℃, sunshine hours ≤3h and average precipitation ≥4mm per event, were identified as the critical thresholds for a disaster event. (2) From 2013 to 2022, the spatial distribution of disaster risk showed higher levels in western and southern Hubei and lower levels in the east and north. The middle risk area accounted for 49.91%, primarily concentrated in the tobacco−growing regions of Shiyan, Shennongjia and Enshi. Lower risk areas (40.48%) were mainly located in the eastern parts of Xiangyang and Yichang, as well as Yunyang and Danjiangkou in Shiyan. Higher risk areas (9.07%) were concentrated in southwestern Hubei, with no areas classified as highest risk. (3) Under the SSP1−2.6 scenario, the higher risk area for low temperature and continuous rainy weather disasters in Hubei’s flue−cured tobacco exhibited a trend of initial increase followed by a decline from 2023 to 2052. During period I (2023−2032), the area increased by 14.60% compared to the baseline period (2013−2022), while during period Ⅲ (2043−2052), it decreased by 12.84% compared to period II (2033−2042). Under the SSP2−4.5 scenario, the change in risk zone region was relatively stable and the middle risk region showed a fluctuating pattern of decrease−increase−decrease. The areas around Shennongjia in western Hubei and the southwestern region remained consistently at the highest risk and higher risk zones for low temperature and continuous rainy weather disasters. These findings provide a scientific basis for optimizing planting layouts and implementing phased disaster prevention and mitigation strategies in tobacco−growing areas of Hubei. To cope with future climate change risks, it is recommended to enhance resilience by improving monitoring and early warning systems, adjusting planting structures, promoting stress−resistant varieties, and strengthening agricultural insurance mechanisms.

As the main rice−producing areas in China, the low temperature is the main meteorological limiting factor for rice production in northeast China. So it’s important to study the impact of extreme low temperatures on rice yield in the three provinces of northeast China for local high and stable rice production and national food security. Taking rice of northeast China as the research object, this paper analyzed the influence of cool days on rice yield in the three provinces of northeastern China from 1980 to 2021, in order to solve the problem of unclear impact of extreme low−temperature events on rice yield, and to provide a reference for the impact assessment of such events on rice yield. The results showed that from 1980 to 2021, the cool days in the four growth stages of rice as transplanting−regreening, regreening−booting, booting−heading and heading−ripening in the three provinces of northeast China all showed a decreasing trend. The abrupt changed of cool days occurred in 2009, and the downward trend became statistically significant since 2016. The cool days during the rice field growing season exhibited an extremely significant negative effect on the relative meteorological yield of rice. Among all growth phases, the transplant−regeneration phase showed a positive effect, whereas the others demonstrated negative effects. The cool days during the booting−heading stage had the greatest contribution rate to the relative meteorological yield, reaching 43%, indicating that the cool days in this period had the greatest impact on the rice yield in the three provinces of northeast China. Among the three provinces of northeastern China, Liaoning exhibited the highest contribution rate of cool days to the relative meteorological yield of rice, at 55%, demonstrating that the cool days in Liaoning had the greatest impact on rice yield. The cool days at different growth stages exhibited a certain interactive effects on the relative meteorological yield of rice, and these interactive effects may amplify the positive contribution of cool days during the transplanting−regreening stage. In future rice production management across the three northeastern provinces, particular attention should be paid to the occurrence of cool days during the booting−heading stage, and corresponding measures should be taken in a timely manner to reduce their effects to ensure high and stable rice production.

The combination of heat and drought stress during the June-August period has become one of the agrometeorological disasters that are constraining the sustainable development of the jujube industry in Xinzheng city of Henan province, an important jujube production base in the Huang−Huai river basin. Based on the ground meteorological observation data from June to August in Xinzheng city from 1981 to 2022 and the high−resolution gridded real−time datasets from June to August in 2010 to 2022, this study used principal component analysis and ordered sample clustering methods to construct the jujube heat and drought index. A heat and drought disaster risk assessment model was developed by applying information diffusion theory to conduct a refined quantitative assessment of heat and drought disasters in the main jujube production area of Xinzheng. The results showed that the heat and drought index of Xinzheng jujube showed an upward trend from 1981 to 2022. The Mann−Kendall trend test showed a significant upward trend in the two periods from 1996 to 2007 and 2012 to 2022 (P<0.05). After the Empirical orthogonal function (EOF) decomposition of the Xinzheng jujube heat and drought index, the first mode accounted for 79.3% of the total variance contribution. Its spatial vector field displayed significant spatial consistency, indicating a consistent upward or downward trend of the heat and drought index across the entire area of Xinzheng city. The probability of mild, moderate and severe heat and drought risk occurring in Xinzheng city during June−August was 5%, 46% and 34%, respectively. Longhu town and the northern part of Mengzhuang town in the north of Xinzheng city, as well as Guanyinsi town and Lihe town in the south of Xinzheng city were high−probability areas for severe heat and drought risk. The probability of moderate heat and drought disaster risk was relatively high in Xindian town, Chengguan, Xinhua, Xinyan, Hezhuang town, Longwang town and Xuedian town in the central part of Xinzheng city, while the probability of mild heat and drought disaster risk was concentrated in Xindian town in the southern part of Xinzheng city. The high−risk areas in the main jujube planting areas of Xinzheng were mainly distributed in the northern part of Mengzhuang town, while the medium−risk areas were distributed in the southern part of Mengzhuang town, the northwest of Xuedian town, the northeast of Longwang town and the southern part of Hezhuang town. 

In order to clarify the relationship between precipitation events and insurance claims and enhance meteorological insurance services, this study utilized 703 insurance claim records and hourly precipitation data from Hunan province (2021−2024). A random forest model was used to construct thresholds for precipitation insurance claims in county−level urban areas in Hunan province, followed by a significance test. The results showed that: （1） the random forest model outperformed XGBoost and LightGBM in precision, Critical success index (CSI) and accuracy, demonstrating superior simulation of insurance claim thresholds. It effectively evaluated the thresholds across different locations and time windows. （2）Cumulative precipitation 120h, 96h and 72h prior to claims, along with the maximum hourly precipitation whthin 120h, showed high feature importance scores, identifying them as key claim−influencing factors. （3）Significant spatial heterogeneity existed in precipitation claim thresholds. The conclusion that 'higher precipitation intensity let to a higher likelihood of claims' did not hold spatially. Low−threshold regions (cumulative 120h precipitation <50mm) accounted for 40% of claimed areas, indicating high sensitivity to precipitation events and vulnerability to disasters. High−threshold regions (cumulative 96h precipitation >65mm) constituted 16%, suggesting claims occur only under intense precipitation. （4）The Chi−square tests confirmed statistically significant relationships between the precipitation insurance claim thresholds of 6 prefecture−level urban areas and 19 county−level cities (districts) level urban areas constructed based on random forests, effective discrimination of claim triggers across precipitation intensities. The research results reveal an inherent correlation between the spatiotemporal distribution of precipitation and insurance claims, which can provide a scientific basis for insurance meteorological warnings and disaster prevention and reduction in Hunan province. 

This study utilized paper−based annual reports (1981−2012) and electronic annual reports (2013−2022) from 653 national agro−meteorological stations across China. Data from 618 stations with continuous yield observations for seven major crops−wheat, rice, maize, oil−seed rape, cotton, soybean and peanut were selected. A comprehensive processing workflow−including standardization of crop names and observation items, imputation of missing values, data series alignment, unit and metadata harmonization and rigorous quality control and validation was applied to develop a high−quality dataset of major crop yields in China for 1981−2022, supporting agricultural climate change research. Results showed that the data availability rate for actual yield observations exceeded 91.0% across all crops, with the accuracy rate >97.0%. Among the 20 yield factor variables, all except winter wheat overwintering mortality rate had availability rates >78.8%, and all exhibited accuracy rate >97.0%. For the 60 yield structure variables, availability rate was >88.6% and accuracy rate exceeded 90.2%. Targeted data correction significantly improved data reporting rates: winter wheat overwintering mortality increased from 12.2% to 61.7%, and maize double−ear rate rose from 52.8% to 95.9%. This dataset provides standardized, high−quality baseline data for agro−ecological studies, climate change research, disaster risk reduction, and agricultural climatic zoning. The comprehensive quality control framework established in this study offers valuable insights for enhancing the quality of agrometeorological datasets.

This study constructed a dataset characterizing the historical variations in eight phenology stages (sowing, emergence, fifth true leaf, bud emergence, bolting, flowering, green maturity and maturity) and their lengths for winter rape, based on long−term phenological observation data (1981−2022) from 13 national agrometeorological observation stations in Jiangxi province, China. Utilizing standardized quality control protocols, kernel density estimation and linear tendency analysis, the dataset comprehensively documented the kernel density distribution patterns of Ordinal day from Jan.1 (DOY) for 8 phenology stages and its 4 lengths across all 13 stations, along with historical trend equations and statistical significance test results. The dataset comprised 256 JPG figures (345MB) organized into 14 sub−folders. This data resource provided critical scientific support for estimating yield potential, optimizing field management practices, refining regional cultivation zoning, mitigating agrometeorological risks and breeding improved cultivars. It is significant practical value for advancing the national strategic objective of "expanding oilseed production capacity" and offering essential data−driven insights to enhance the climate resilience of winter rape production systems in Jiangxi province. The dataset is publicly accessible via the ScienceDB platform at https://doi.org/10.57760/sciencedb.22236.