The LDMI model to decompose the driving and restraining factors of carbon emissions was developed, while using ridge regression to fit an extended STIRPAT model to predict agricultural carbon emissions in Jiangsu province. Five scenarios were established, including baseline, baseline with weak low−carbon development, baseline with strong low−carbon development, low development with strong low−carbon and low development with weak low−carbon. The objective was to clarify the driving mechanisms of agricultural carbon emissions in Jiangsu province, systematically simulated the changing trends of its agricultural carbon emissions from 2021 to 2030, and provided a basis for formulating differentiated emission reduction pathways. The results showed that agricultural production efficiency, agricultural industrial structure and labor force scale played a restraining role in agricultural carbon emissions in Jiangsu from 2006 to 2020. The cumulative contribution of these factors to carbon emissions reached −3259.99×10⁴t, −81.40×10⁴t and −845.73×10⁴t, with agricultural production efficiency being the primary restraining factor. In turn, the level of agricultural economic development and agricultural labor productivity served as driving factors from 2006 to 2020. The cumulative contribution of these factors to carbon emissions reached 1771.62×10⁴t and 1025.74×10⁴t, with agricultural economic development being the main driver. From 2006 to 2020, the restraining effect of labor force size and the promoting effect of agricultural economic development and agricultural labor productivity were both showing a weakening trend in Jiangsu. Under the five scenarios, agricultural carbon emissions in Jiangsu province exhibited a downward trend, among which the scenario of baseline with strong low−carbon development demonstrated the greatest potential for carbon emission reduction, indicating that a robust and green economic development model was the optimal path for Jiangsu province’s future. Therefore, in order to reduce the Jiangsu agricultural carbon emissions, it is necessary to actively develop low−carbon technologies to improve the efficiency of agricultural production, continuously to optimize the industrial and energy structure, to balance economic development and rural population transfer, and to provide quantifiable and actionable practical paths for carbon neutrality of agriculture. 

This study examined ten representative toilet models across four categories in rural China. A full life cycle carbon accounting model was constructed, covering toilet construction, fecal collection, storage, treatment (operation) and resource utilization. Emission factor method was used to quantify carbon emissions at each stage, identify key emission sources and propose mitigation strategies to support emission reduction in rural areas under China's dual−carbon goals. The results showed that: (1) carbon emissions varied across ten toilet models. Between the two sanitary dry toilets, the urine−diverting dry toilet had lower per capita carbon emissions (57.62kgCO₂e·cap⁻¹·y⁻¹) and achieved a mitigation effect of 19.10kgCO₂e·cap⁻¹·y⁻¹ through resource utilization. Among the six sanitary flush toilets, the biogas−linked toilet had the lowest per capita emissions (41.82kgCO₂e·cap⁻¹·y⁻¹) and the highest mitigation potential (176.74kgCO₂e·cap⁻¹·y⁻¹), the three−septic−tank toilet had the highest per capita emissions (113.25kgCO₂e·cap⁻¹·y⁻¹), unimproved flush toilets and pit latrines emitted 185.67kgCO₂e·cap⁻¹·y⁻¹and 48.09kgCO₂e·cap⁻¹·y⁻¹, respectively. These two models had no mitigation. (2) The main emission stages were toilet construction and fecal treatment (operation), accounting for 8.43%–56.11% and 48.59%–89.88% of total emissions respectively (excluding biogas−linked toilet). Major carbon sources included embodied carbon in construction materials, methane (CH₄) released during anaerobic degradation, and nitrous oxide (N₂O) conversion. (3) Strategies for fecal sludge−related carbon mitigation include sanitary upgrades, substitution with low−carbon materials and optimization of resource utilization pathways. This study identified the emission hot spots and feasible mitigation measures for rural toilet renovation through life cycle carbon accounting, and provided a scientific basis for advancing fecal waste resource utilization and supported China’s carbon peaking and neutrality goals.

Stable forests exhibit high vegetation integrity and a relatively stable microclimate. Revealing the effect of changes in the greenness of stable forests on surface albedo can provide a quantitative basis for understanding energy exchange processes and forest responses to climate change. This study focused on the stable forests of Guizhou province, utilizing MODIS surface albedo, Enhanced vegetation index (EVI) and Fractional vegetation cover (FVC) data from 2001 to 2022. By employing Theil-Sen trend analysis, Mann-Kendall trend testing, correlation analysis and relative importance analysis, the temporal and spatial variations of surface albedo, EVI and FVC in Guizhou's stable forests were explored at monthly, annual and growing season scales, as well as the influence of greenness changes across different forest types on surface albedo. The results showed that: (1) from 2001 to 2022, the monthly surface albedo of evergreen broadleaved forests, evergreen needle−leaved forests and deciduous broadleaved forests in Guizhou exhibited insignificant patterns, peaking in July. The monthly average surface albedo ranked as follows: deciduous broadleaved forests > evergreen broadleaved forests > evergreen needle-leaved forests. (2) During 2001-2022, Guizhou's stable forests exhibited a significant interannual greening trend (P < 0.05). The spatial variations of surface albedo on both interannual and growing-season scales showed an increasing trend in the northwest and a decreasing trend in the southeast. Evergreen broadleaved forests were the dominant type driving the increase in surface albedo and forest greening (contributing >55%). (3) From 2001 to 2022, the surface albedo of Guizhou's stable forests exhibited a significant positive correlation (P<0.05) with EVI and FVC, indicating that forest greening did not reduce surface albedo but rather enhanced its shortwave radiation reflectance capacity. Among forest types, evergreen broadleaved forests demonstrate the most pronounced positive driving effect. 

Straw resource utilization is a key approach for enhancing soil carbon sequestration and regulating nutrient cycling. This study was conducted within a long−term experimental platform for low−carbon agriculture, established by the Shanghai Academy of Agricultural Sciences in 2012. Three straw management practices, including straw removal (CK), direct straw return (SR) and straw biochar return (BC) were compared to assess effects on SOC dynamics in 2024, with particular emphasis on the mineral−associated organic carbon (MAOC) and particulate organic carbon (POC) fractions. The objective was to elucidate the mechanisms through which these practices influence SOC stabilization, providing a scientific basis for enhancing soil carbon sequestration and promoting straw resource utilization. The results showed that: (1) compared with CK treatment, both SR and BC treatments significantly increased SOC content by 25.3% (P<0.05) and 41.4% (P<0.05), respectively, indicating substantial carbon sequestration effects under both straw return practices. (2) Regarding carbon fractions, both SR and BC treatments effectively increased POC content, with increases of 49.1% (P<0.05) and 111.5% (P<0.05) compared to the CK treatment, respectively. Moreover, both straw return practices significantly promoted the formation of MAOC, with increases of 19.7% (P<0.05) and 25.3% (P<0.05) relative to the CK treatment. However, no significant difference was observed between SR and BC treatments in terms of MAOC increase, which may be attributed to competing processes involving microbial activity and homeostasis. In conclusion, straw biochar return not only facilitate rapid carbon sequestration by increasing the POC content, but also enhance long−term carbon sink capacity by promoting MAOC formation. These findings proposed a path toward more sustainable and efficient straw resource management in agricultural systems.

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides systematic data support for global climate research, and downscaling techniques can address the limitation of its spatial resolution, which is insufficient for climate projections in regions with complex terrain. This study focused on the upper Yangtze River basin, systematically evaluated the simulation capabilities of eight CMIP6 climate models for precipitation and mean temperature during the baseline period (2015−2020) using Taylor diagrams, and constructed a multi−feature fusion downscaling model based on Deep Neural Networks (DNN) to downscale the optimized climate models spatially, and analyzed the variation characteristics of precipitation and mean temperature in the basin under four future scenarios (SSP1−2.6, SSP2−4.5, SSP3−7.0 and SSP5−8.5) in 2050. The results showed that the Ec−Earth3−Veg climate model performed best at simulating precipitation in the upper Yangtze River basin from 2015 to 2020, while the ACCESS−ESM1−5 model performed best at simulating mean temperature over the same period. The multi−feature fusion spatial downscaling model, based on a DNN, increased the horizontal resolution of precipitation in the region from 0.7031°×0.7018° to 0.008°×0.008° for both the 2015−2020 and 2050 periods, and the horizontal resolution of mean temperature from 1.875°×1.250° to 0.018°×0.018°. The average correlation coefficient between the downscaled precipitation and mean temperature results and the observed data was 0.47 and 0.88, respectively. Compared with the baseline period, precipitation in 2050 was projected to increase significantly under the SSP1−2.6 and SSP5−8.5 scenarios, while it was expected to decrease significantly under the SSP2−4.5 and SSP3−7.0 scenarios, with the largest decrease of 15.51% under SSP3−7.0. The mean temperature was projected to rise relative to the baseline period in all scenarios, with greater warming in the higher-emission scenarios. This study provides data to support the scientific allocation of water resources and the precise management of agricultural production in the upper Yangtze River basin. It serves as a scientific reference for risk assessment and response strategies to extreme regional climate events.

Under the background of frequent and enhanced extreme weather events caused by global climate change, the risk of agro−meteorological disasters faced by rice production in China is becoming more serious, posing a potential threat to national food security. In order to systematically reveal the impact of extreme weather on rice production, based on the climatic environment and planting characteristics of the three major rice−producing areas in northeast China, the Yangtze River basin and the southeast coast of China, this study comprehensively analyzed the impact of single agrometeorological disaster and compound agricultural disaster on rice yield. The results showed that the main single meteorological disasters affecting rice production were extreme high temperature and low temperature, followed by drought and floods. The stress response of these disasters to rice significantly affected its growth and development processes, with dry matter accumulation and distribution due to differences in growth stages. Compound agricultural disasters such as high temperature and drought, low temperature and low light were more severe, complex and unpredictable than single disasters, causing more severe damage to rice yield. In production practice and scientific research, related research still faced problems such as imperfect long−term monitoring systems of the disaster chain, limited applicability of meteorological indicators, and insufficient analysis of multi−factor interactions and improved accuracy of crop model simulations. In the future, it is necessary to further deepen the integration of multi−source data, construct a composite disaster risk assessment model, and carry out research on disaster−causing mechanisms, refined index revision, multi−factor interaction analysis, crop model accuracy improvement and adaptive regulation technology, so as to provide theoretical basis and decision support for ensuring the climate resilience and sustainable development of China's rice industry.

This study investigated the spatiotemporal patterns of climatic indices and maize yields across 69 stations in North China from 1980 to 2018. A multiple regression model was established to quantify the contributions of temperature, precipitation (both in normal and extreme states), and solar radiation to maize yields. Additionally, the potential impacts of future climate change on maize yields were projected under two future scenarios: SSP1−2.6 and SSP5−8.5. The results showed that: (1) from 1980 to 2018, the growing degree days (GDD) and heat degree days (HDD) of maize exhibited a significant upward trend, with rates of 32.4℃·d·10y⁻¹ and 4.0℃·d·10y⁻¹, respectively. Conversely, freezing degree days (FDD) and solar radiation showed a significant downward trend, declining at rates of 3.2℃·d·10y⁻¹ and 59MJ·m⁻²·10y⁻¹, respectively. Spatially, precipitation and maximum daily precipitation were higher in the southeast than in the northwest, while the longest consecutive dry days were observed in the central North China. Maize yield significantly increased at a rate of 57.5kg·ha⁻¹·y⁻¹. (2) The contribution rates of climatic indices to maize yield were, in descending order: HDD (−28%) > precipitation (23%) > maximum daily precipitation (−16%) > solar radiation (−14%) > GDD (12%) > consecutive dry days (−6%) > FDD (1%). The total contribution of normal and extreme precipitation (45%) was greater than that of normal and extreme temperature (41%). (3) Under the SSP1−2.6 scenario, 78% of the study area was projected to experience negative meteorological yields between 2031 and 2060, slightly decreasing to 72% by 2100. In the more severe SSP5−8.5 scenario, this proportion was projected to increase from 80% (2031−2060) to a remarkable 94% (2071−2100). Overall, HDD exerts the most significant impact, leading to decreased yields, while precipitation plays a role in boosting yields, with its influence outweighing that of temperature. Future climate change could potentially result in substantial reductions in maize production across North China.

Based on daily meteorological data from 122 weather stations on the Qinghai−Tibet plateau from 1981 to 2020 and agricultural meteorological data from 4 agrometeorological stations on highland barley production, this study used an elevation component separation model to separate the elevation component of the climate tendency rates of temperature at each meteorological station. Using a parameter−calibrated APSIM−barley model, the study simulated the growth period and light−temperature potential yield of highland barley in the region. The stability coefficient of the light−temperature potential yield was used as an indicator to divide potential highland barley planting areas on the Qinghai−Tibet plateau, clarifying the relationships between the elevation component of temperature climate tendency rates and simulated highland barley growth period, light−temperature potential yield and stability coefficient of light−temperature potential yield. This study aimed to explore the impact of elevation−dependent warming on highland barley growth on the Qinghai−Tibet plateau, providing a theoretical basis for alleviating the conflict between food demand and limited arable land resources in the region and ensuring food security. The results showed that the APSIM−barley model after parameter calibration could accurately simulate highland barley growth. The coefficients of determination R² and RMSE between simulated and measured values were 0.764 and 2.17d for the duration from sowing to flowering, 0.641 and 1.85d for the duration from flowering to maturity, and 0.554 and 473.88kg·ha⁻¹ for the yield. APSIM−barley model could be used for subsequent analysis. The zoning results indicated that high and relatively high highland barley planting potential areas were mainly distributed in the northeastern, central−eastern and southern parts of Qinghai, the northern, northwestern and central parts of the Tibetan areas in western Sichuan, and the southern and southwestern regions of the Tibet Autonomous region. Overall, the simulated highland barley growth period, potential yield and stability coefficient of the potential yield showed the strongest positive correlation with the elevation component of the mean temperature climate tendency rates. The correlation coefficient r was 0.25 and 0.36 for the number of days from sowing to flowering and from flowering to maturity, respectively, while it was 0.37 and 0.36 for the potential yield and the stability coefficient of potential yield, respectively. This indicated that the elevation−dependent warming during the study period had been favorable for highland barley growth, with the greatest effect arising from the mean temperature climate tendency rate. By separating the elevation component of temperature climate tendency rates, this study provides a more accurate assessment of the impact of elevation−dependent warming on highland barley growth on the plateau. In the future, this can inform the adjustment of highland barley planting layouts, and the findings can offer scientific guidance for highland barley production on the Qinghai−Tibet plateau.

Based on daily observational data from ten national meteorological stations in the Chishui River basin (1981–2024) , the vapor pressure deficit (VPD) was calculated using the Tetens formula. To assess the effect, annual data on sowing area, yield and the long−term average growth period of liquor−making sorghum in Renhuai (2006–2024) were utilized. The temporal–spatial characteristics of VPD were analyzed using trend analysis, Mann–Kendall test and wavelet analysis, followed by a quantitative assessment of the VPD−induced stress on liquor−making sorghum production in Renhuai. The results showed that: (1) from 1981 to 2024, the VPD in the Chishui River basin exhibited a highly significant increasing trend at the annual, spring and summer scales (P<0.01), with rates of 0.005kPa·10y⁻¹, 0.007kPa·10y⁻¹ and 0.007kPa·10y⁻¹, respectively. A significant increase was also observed in winter (0.004kPa·10y⁻¹, P<0.05), while the autumn trend was not significant. The greatest change occurred in summer (0.105 to 0.207kPa), and the smallest in winter (0.074 to 0.124kPa). Following an abrupt change observed from 2001 to 2004 across annual and seasonal scales, the VPD showed a consistent increase, and its variability increased over the subsequent decades. (2) From 1981 to 2024, annual and seasonal VPD consistently showed a spatial pattern of low in the northwest and high in the southeast. This pattern was projected to strengthen over the next 3–7 years of annual and seasonal VPD in the Chishui River basin. (3) During the period of 2006–2024, every 0.01kPa increase in the mean VPD of the liquor−making sorghum growing season (April–August) in Renhuai reduced the yield by 108.8kg·ha⁻¹. The increase of VPD had a direct negative effect on the yield and indirectly amplified the yield loss by intensifying the combined stresses of air temperature and relative humidity. Soil−water conservation and irrigation infrastructure are recommended to mitigate the risks associated with rising VPD.

Based on daily ground meteorological observation data from 101 meteorological stations, historical disaster data, and maize production data in Xinjiang from 1961 to 2020, this study clarified the spatiotemporal characteristics of high temperature heat damage during the maize flowering stage under the background of climate change by using linear trend analysis and spatial interpolation analysis methods. At the pentad scale, it further revealed the changing patterns of such heat damage during different periods in southern and northern Xinjiang, aiming to provide a reference for maize production management and agrometeorological services in Xinjiang. The results showed that: (1) from 1961 to 2020, moderate high temperature heat damage was the dominant type during the maize flowering stage in Xinjiang, followed by light heat damage, exhibiting a spatial distribution pattern of higher occurrence in the east and lower in the west. Regions with a heat damage frequency exceeding 60% were mainly concentrated in western Changji Hui Autonomous prefecture in northern Xinjiang and central Bayingolin Mongol Autonomous prefecture in southern Xinjiang. (2) High temperature heat damage during the maize flowering stage in Xinjiang showed an interdecadal intensification trend. In the 2010s, the total frequency of heat damage events of all levels increased by 6.6 pp compared to the 1960s. Both the station ratio and the number of occurrence days showed an increasing trend in southern and northern Xinjiang, with a greater rate of increase observed in northern Xinjiang than in southern Xinjiang. (3) The widest spatial extent of heat damage occurred during the 4^th pentad in July in both southern and northern Xinjiang, with station ratios of 36.0% and 27.0%, respectively. Compared to period I (1961−1990), the pentad-based station ratio of heat damage during the maize flowering stage in Xinjiang generally increased during period II (1991−2020), and the pentad with the highest station ratio showed a trend of occurring earlier.

By using the price monitoring data from 94 agricultural wholesale markets and concurrent hail observation data from March to May during the period 2017 to 2024, the impact of hail weather on vegetable prices in Guizhou province was analyzed based on the VAR model, which revealed how hail intensity indicators (including hail frequency, average hail diameter and maximum hail diameter) affect vegetable price fluctuations. The study aimed to provide a scientific basis for forecasting vegetable prices and assessing the benefits of hail suppression in Guizhou. The results showed that: (1) the weekly average price of vegetables showed a significant positive correlation with days of hail, the correlation coefficient was 0.94 (P＜0.05), and the weekly average price of vegetables rose with the increasing number of days of hail in a week, each additional hail day per week increased the weekly average price of vegetables by 0.14yuan·kg⁻¹. (2) For the same number of hail days, the weekly average price of vegetables increased with larger hail diameters. Within the same hail diameter range, the weekly average price of vegetables increased as the number of hail days increases. (3) Granger causality tests indicated that neither days of hail, average hail diameter and maximum hail diameter significantly predicted the weekly average price of vegetables fluctuations as none of them passed the significance test at the 0.1 level. (4) Regarding the impact on the weekly average vegetable price fluctuation range, the influencing factors are ranked as follows: the days of hail>the average hail diameter>the maximum hail diameter. Regarding the impact on the duration of the weekly average vegetable price fluctuations, the influencing factors are ranked as follows: the days of hail<the average hail diameter≈maximum hail diameter. (5) The contribution of hail frequency to the weekly average vegetable price fluctuations exceeded that of average hail diameter, which was greater than that of maximum hail diameter. Collectively, these three factors accounted for 4.021% contribute to the weekly average vegetable price. 

 Based on the daily maximum temperature and minimum temperature data of 38 meteorological stations in Xizang from 1981 to 2023, 11 extreme temperature indices were selected for this study. Methods including trend analysis, the Mann−Kendall mutation test, and wavelet period analysis were employed to systematically investigate the spatiotemporal evolution characteristics of extreme temperature indices in Xizang. The study aimed to clarify the core features, mutation points and periodic response mechanisms of these indices on the plateau under climate warming, providing a scientific reference for climate change adaptation, ecological environmental protection and disaster prevention and mitigation policy formulation in the region. The results showed that: (1) temporally, three cold indices from 1981 to 2023 (frost days FD, cool nights TN10p and cold spell duration index CSDI) across Xizang showed a significant decreasing trend (P<0.01), with FD exhibiting the most pronounced decline. In contrast, warm indices (warm days TX90p, warm nights TN90p and warm spell duration index WSDI) and extreme indices (monthly maximum of daily minimum temperature TXn, monthly minimum of daily maximum temperature TNx, annual minimum of daily minimum temperature TNn and annual maximum of daily maximum temperature TXx) all showed a significant increasing trend (P<0.05), with TN90p showing the most notable increase. Other indices (daily temperature range DTR) exhibited a fluctuating decreasing trend. Mutation analysis revealed that the cold indices underwent significant shifts in the late 1990s and continued to decline thereafter, while the warm indices surpassed the significance threshold after 2000 and continued to intensify. (2) From 1981 to 2023, all 11 extreme temperature indices in Xizang exhibited multi−scale periodic oscillations. The main characteristic was that the cold indices persistently remained in a the low−temperature phase on short−term scales, whereas the warm indices, extreme indices and other indices mostly showed sustained high−temperature features at corresponding short−term scales. (3) Spatially, the cold indices (FD, TN10p and CSDI) from 1981 to 2023 showed a significant decreasing trend in Lhasa, Shannan and the western and eastern parts of Nagqu. The warm indices (TX90p, TN90p and WSDI) exhibited significant increases across almost the entire Xizang. Over 80% of the region displayed an increasing trend for the extreme indices (TXn, TNx, TNn and TXx). At the same time, the DTR index decreased in the western and eastern parts of Xizang, showing distinct spatial heterogeneity. High−altitude regions responded particularly significantly to climate change, with a sharp decrease in the cold indices in the central−western Nagqu region contrasting distinctly with a dramatic increase in the warm indices in the northeastern part of the region, highlighting clear spatial contrasts.

Apples are of great importance in the global and Chinese fruit industries. Against the backdrop of climate change, frost disasters during the apple blossom period are occurring with increasing frequency and severity, posing a serious threat to yield and quality. Classification, summarization and systematic analysis were employed in this study. Based on the concepts and indicators that define apple blossom frost in China, it reviewed research progress on frost disaster loss and risk assessment during this critical period, analyzed existing issues and proposed future research priorities. The results show that the single factor about apple blossom frost indicator based on the daily minimum temperature was currently the most widely used. The assessment of frost damage during apple blossom was dominated by statistical models and the integration of various methods had been effective in identifying key disaster-causing factors and constructing loss models. Significant progress had also been made in assessing the impact of frost on yield during apple blossom using the STICS model. The risk assessment of apple blossom frost involved multiple factors and often suffered from subjectivity and uncertainty, with relatively limited research and applications. In contrast, notable achievements had been made in the narrow risk assessment of apple blossom frost and temporal exposure risk assessment. It was projected that frost risks would decrease in the Bohai bay and the southwestern high-altitude of cold regions, while it would increase in the Loess plateau region. While the risk might decrease in Xinjiang production area, the intensity of the frost was expected to intensify. The earlier flowering period of apple trees had been identified as a central cause of increased frost intensity in temperate growing regions. In the future, the construction of comprehensive and localized apple blossom frost indicators and the establishment of multi-model coupled assessment methods will be the core and key approaches for frost loss and risk assessment during apple blossom period in China. 

Based on observational data from 9 meteorological stations in Shaanxi province and the CRU TS v4.09 monthly grid climate dataset released by the Climate Research Centre of the University of East Anglia in the United Kingdom from 1981 to 2019, the XGBoost machine learning algorithm was introduced. Taking 1981−2010 as the training period and 2011−2019 as the testing period, a machine learning correction model was established. A correction was applied to the hydrothermal meteorological data from the CRU TS v4.09 monthly grid climate data set for Shaanxi province from 1912 to 1949. Combined with the Standardized precipitation evapotranspiration index (SPEI), the meteorological drought index of Shaanxi from 1912 to 1949 was reconstructed, and the spatiotemporal evolution characteristics of meteorological droughts were analyzed to provide a reference for regional historical drought reconstruction. The results showed that: (1) the hydrothermal meteorological correction model constructed by the XGBoost algorithm exhibited satisfactory simulation performance, with precipitation R² of 0.77 and RMSE of 29.27mm, and mean temperature R² of 0.92 and RMSE of 2.08℃ during the testing period. (2) Based on the hydrothermal meteorological correction model, the monthly grid based hydrothermal meteorological data of CRU TS v4.09 in Shaanxi province from 1912 to 1949 were corrected. From 1912 to 1949, precipitation in Shaanxi showed a slightly fluctuating pattern of increasing in the north and decreasing in the south, with a nonsignificant trend. The mean temperature exhibited an extremely significant increasing trend (P<0.05). (3) The drought coverage and intensity in Shaanxi were relatively low from 1912 to 1921, but rose from 1922 to 1949 and showed a characteristic of multi peak oscillation. In 1941, the drought coverage and intensity reached the peak of the entire region. (4) From 1912 to 1949, drought frequency increased while drought intensity decreased from south to north across the province. High-frequency areas of severe drought were mainly distributed in Guanzhong and parts of southern Shaanxi. (5) The overall drought tendency of Shaanxi intensified from 1912 to 1949, and southern Shaanxi witnessed the most significant trend. In summary, the XGBoost algorithm can effectively correct historical gridded meteorological data, providing technical support and methodological reference for the reconstruction and evolution analysis of long−term historical meteorological droughts.

Based on daily meteorological observations from approximately 1800 meteorological stations and agrometeorological observations from nearly 300 agrometeorological stations across the major production areas of the summer harvested grain and oil crops (winter wheat and rapeseed) in 2026, the methods of statistical analysis, satellite remote sensing and crop model simulations were utilized to obtain the climatic suitability, agrometeorological disaster indices and remote sensing based crop index to assess the impact of meteorological conditions in 2026 on the growth and yield of winter wheat and rapeseed. The results showed that the sowing period in the northern winter wheat region was significantly delayed due to persistent rainy weather in the autumn of 2025, resulting in relatively weak seedling condition under the pre−winter stage. However, favorable thermal and precipitation conditions in the winter of 2025 and spring of 2026 facilitated the upgrading transformation of wheat seedling and construction of high−yielding canopy. The winter wheat seedling continued to improve and reached the expected yield. Since rapeseed was sown in the autumn of 2025 across the southern major production zone, climatic resources, including sunshine, thermal and precipitation matched well in most periods. However, periodical low−temperature cold surged in the winter of 2025 caused frost damage to some rapeseed in the middle and lower reaches of Yangtze River region. Additionally, waterlogging owing to excessive moisture stress since late March adversely affected yield formation. Overall, the meteorological conditions during the winter wheat growing season were characterized by bad in the previous growing stages followed by good in the later stages, which was the opposite for rapeseed.