Based on its attribute of agricultural impact properties, agrometeorological disaster risk early warning integrates forecasting of catastrophic weather with dynamic monitoring and assessment of exposure and vulnerability of agricultural hazard−bearing bodies. It estimates the impact of disasters on agriculture and provides early warning services. This study provided an overview of relevant concepts and terminology for early warning of agrometeorological disaster risk. The three core elements, hazard, exposure and vulnerability, were then quantitatively evaluated to systematically organize agrometeorological disaster risk identification techniques for early warning services and develop operational technical processes for agrometeorological disaster risk early warning. Taking the risk early warning of dry−hot wind disaster for winter wheat (12–18 May 2025) as a case study, this study demonstrated the full process of dry−hot wind disaster risk assessment, risk grade identification and early warning issuance, which was based on 10d forward meteorological element forecasts. The results showed that, unlike conventional technical methods for agrometeorological disaster risk assessment (which focused on evaluating the probability and impacts of historical disasters), agrometeorological disaster risk early warning predicted the likelihood of future disasters and their adverse effects on crops, while issuing corresponding warning information. Its assessment and judgment of disaster hazard and exposure was grounded in real−time meteorological forecasts and dynamic analysis of agricultural condition monitoring, thus exhibiting real−time and dynamic characteristics. The established operational technical processes of agrometeorological disaster risk early warning involved the following key steps: calculation and assessment of disaster risk indices, classification of risk levels and formulation of criteria for early warning issuance. When an agrometeorological disaster was projected to occur, the work of calculating a risk index was initiated: each risk component was evaluated and calculated individually to determine an agrometeorological disaster risk index and the risk assessment results were derived through risk grade judgment. Once the area proportion of risk levels at or above "relatively high risk" met the area ratio specified in early warning issuance criteria, a risk early warning was issued to the public. Notably, specific methods for risk index calculation and early warning issuance criteria varied across different agrometeorological disasters. For current risk early warning of dry−hot wind disaster for winter wheat: (1) exposure was determined via dynamic identification of winter wheat in the flowering and grain−filling stage. (2) Hazard was assessed using various combinations of dry−hot wind days and grades in the upcoming 10d. (3) Vulnerability was evaluated based on dry−hot wind damage reference values specified in the meteorological industry standard Grades of Wheat Dry−Hot Wind Disasters. (4) The early warning issuance criterion was defined as follows: the area with dry−hot wind disaster risk at or above the relatively high level accounts for no less than 15% of the total wheat sowing area in the northern winter wheat region.

Based on time−series data from the Puyang Statistical Yearbook, this study employed the carbon emission coefficient method to estimate agricultural carbon emissions in Puyang city over the past 13 years (2009−2021). The Tapio decoupling model was applied to analyze the decoupling relationship between agricultural carbon emissions and economic growth, while the LMDI decomposition method was used to identify key driving factors, providing theoretical references for the development of green and low−carbon agriculture. The results showed that agricultural carbon emissions exhibited a fluctuating trend of "increase−decrease−increase−decrease," with an annual average of 33.55×10⁴t. Emissions peaked in 2018 (43.70×10⁴t) and declined to 30.23×10⁴t by 2021 (a 30.82% reduction from 2018). Carbon emission intensity showed a consistent downward trend from 2009 to 2016 followed by a temporary rebound during the 2017−2018 period. Subsequently, it continued to decline from 2018 to 2021, reaching the study−period minimum of 0.1078t·10⁻⁴yuan in 2021, reflecting significant progress in Puyang's agricultural low−carbon transformation. The emission structure was dominated by chemical fertilizers (73.51%), followed by agricultural plastic films (11.35%), diesel (5.88%) and pesticides (5.78%) and so on. The decoupling relationship evolved in stages: weak decoupling dominated in 2010−2014, followed by strong decoupling (2015–2016), negative decoupling (2017−2018) and a return to strong decoupling (2019−2021). LMDI decomposition revealed that agricultural production efficiency (33.78%), regional industrial structure (10.02%) and labor scale (3.15%) were the primary drivers of emission reduction, whereas regional economic growth (44.69%), urbanization (7.02%) and agricultural structure (1.34%) contributed to emission increases. Based on the research findings, it is recommended to promote low−carbon and high−quality agricultural development in Puyang city through technological innovation, structural optimization and enhancing the low−carbon production capacity of farmers.

Based on daily observations of 0cm ground temperature and average temperature from 78 meteorological stations in the three provinces of northeast China (Heilongjiang, Jilin and Liaoning) from 1961 to 2020, this study employed trend analysis and correlation analysis to analyze the spatial−temporal variation characteristics of 0cm ground temperature and average temperature in the region. The results showed that the high−value areas of 0cm ground temperature were distributed further north compared to that of average temperature from 1961 to 2020. From 1991 to 2020, high−temperature zones for both 0cm ground temperature and average temperature shifted northward relative to that of in the period 1961−2020. The decadal tendency of 0cm ground temperature and average temperature during 1961−2020 were 0.75℃·10y⁻¹ and 0.30℃·10y⁻¹, respectively, with significant level of 0.01. The increasing trend of 0cm ground temperature during 1991−2020 (1.30℃·10y⁻¹) was higher than that of 1961−1990 (0.20℃·10y⁻¹), whereas the trend for average temperature (0.15℃·10y⁻¹) was lower than that of 1961−1990 (0.27℃·10y⁻¹). The seasonal decadal tendency of 0cm ground temperature during 1961−2020 were 0.56℃·10y⁻¹ in spring, 0.37℃·10y⁻¹ in summer, 0.49℃·10y⁻¹ in autumn and 1.54℃·10y⁻¹ in winter, with the significant level of 0.01. It showed a slight trend during 1961−1990 but a significant increasing trend during 1991−2020. The seasonal decadal increasing trend of average temperature during 1961−2020 were 0.35℃·10y⁻¹ in spring, 0.21℃·10y⁻¹ in summer, 0.26℃·10y⁻¹ in autumn and 0.41℃·10y⁻¹ in winter, with the significant level of 0.01. During the period of 1991−2020, the increments for spring, summer and autumn were larger than those of in the period 1961−1990, while the winter increment was smaller. From 1961 to 2020, correlations between 0cm ground temperature and average temperature for annual and seasons in the three provinces of northeastern China were extremely significant, with correlation coefficients being 0.99, 0.95, 0.88, 0.99 and 0.98, respectively. The differences between 0cm ground temperature and average temperature from 1961 to 2020 were 2.1℃ annually, 2.3℃ in spring, 3.8℃ in summer, 1.3℃ in autumn and 0.9℃ in winter. The difference was greater in 1991−2020 than that in 1961−1990, and was largest in summer, followed by spring, autumn and winter. 

This study constructed evaluation index systems for agricultural water poverty and food production vulnerability based on annual natural, social and economic data from 20 prefecture−level cities in the Yellow river Ω−shaped bend from 2010 to 2023. The entropy method, the coupling coordination degree model, and the Geodetector were employed to systematically analyze the spatio−temporal evolution characteristics and driving factors of the coupling coordination degree between agricultural water poverty and food production vulnerability, to provide scientific support for the high−quality development of agriculture in the Yellow river basin. The results showed that, (1) from 2010 to 2023 in the Yellow river Ω−shaped bend, the agricultural water poverty index exhibited a significant upward trend, increasing from 0.25 to 0.32 (P<0.05), indicating a shift from moderate to low poverty levels. The food production vulnerability index showed a significant downward trend, decreasing from 0.55 to 0.41 (P<0.05), indicating a shift from relatively high to moderate vulnerability levels. (2) From 2010 to 2023, the annual average coupling coordination degree between agricultural water poverty and food production vulnerability in the Yellow river Ω−shaped bend was 0.59, indicating a moderate level overall. However, significant spatial heterogeneity was observed, with a pattern characterized by “higher values in the southeast and lower values in the northwest”. The southeastern erosion zone in the Yellow river Ω−shaped bend recorded the highest and most stable coordination degree (0.61); the northwestern desert zone fell in the middle (0.58). The northern Hetao plain zone exhibited pronounced fluctuations with notable internal disparities, ranging from 0.53 to 0.61. (3) The regional differences in the coupling coordination degree between agricultural water poverty and food production vulnerability in the Yellow river Ω−shaped bend, fundamentally reflected the alignment between water resource supply and food production demand. Natural background conditions formed the basis of coordination, with annual precipitation and water availability per unit cultivated land area being the dominant factors. Agricultural technology and management factors, such as water−saving irrigation ratio and reservoir capacity, could effectively compensate for natural deficiencies but required alignment with resource endowments. In summary, the agricultural water poverty and food production vulnerability in the Yellow river Ω−shaped bend were highly coupled, and singular improvements in technology or resources were insufficient. A comprehensive regulation with multiple measures is required to enhance regional coordination resilience.

Real−time monitoring of the growth and harvesting progress of major grain crops (corn, rice and soybean) is crucial for national food security and precise smart agricultural services. Based on remote sensing images and agricultural observation data, this study established a self−developed crop growth grading model (CGG) and harvest monitoring model (HM) by combining the ten−day and daily normalized difference vegetation index (NDVI) datasets in the major grain−producing areas of Heilongjiang province from 2019 to 2023. These models were applied to analyze the spatiotemporal evolution characteristics of major grain crop growth and harvest areas, aiming to improve the capability of regional smart meteorological services in guaranteeing food security and boosting regional agricultural development. The results showed that: (1) the self−developed CGG and HM models could effectively identify the growth status and harvesting conditions of major grain crops in the study area from 2019 to 2023, with an overall mean absolute percentage error (MAPE) lower than 25.0%. (2) Simulations via the CGG model revealed a significant year−by−year improvement in the growth of major grain crops in the major grain−producing areas of Heilongjiang Province from 2019 to 2023. During the crop growing season of 2023, the periods with excellent and poor growth status accounted for 43.3% and 8.3% of the entire growing season, respectively. (3) Based on the HM model, by late October from 2019 to 2023, most areas of the major grain−producing areas in Heilongjiang province had completed the harvesting of major grain crops, and the harvesting progress of soybeans was slightly later than that of corn and rice. It is demonstrated that the self−developed CGG and HM models can effectively evaluate the growth and harvesting status of major grain crops in the major grain−producing areas of Heilongjiang province, and the crops showed a favorable growth trend with timely harvesting in the region from 2019 to 2023.

To provide guidance for high−quality rice cultivation management under future climate change, a quantitative model between the average temperature during the grain−filling period (T_ave) and rice quality traits was constructed and validated using an optimized Gaussian mixture model (GMM) and Mean−Shift clustering algorithm. In this study, a staged sowing experiment was conducted in Changsha county of Hunan province in 2022 using 53 different mid−and late−season indica rice varieties. Eight sowing dates were set, including May 1, May 11, May 21, May 31, June 21, July 1, July 11 and July 21. The results showed that grain filling temperature significantly affected rice quality. Among different sowing dates, the coefficients of variation (CVs) for chalky grain rate and chalkiness degree were 39.0% and 48.6%, respectively, reflecting the most pronounced response to temperature. The CVs for alkali spreading value, gel consistency, transparency, and head rice rate were 23.9%, 20.2%, 17.5% and 8.9%, respectively, indicating a moderate response. In contrast, the amylose content, milled rice rate and brown rice rate showed minimal sensitivity, with CVs of only 4.0%, 2.5% and 0.8%, respectively. Based on the GMM and Mean−Shift clustering algorithms, the relational models between grain−filling temperature and rice quality traits were established across the eight sowing dates. R² values of the fitting functions exceeded 0.900 for chalkiness degree, gel consistency and alkali spreading value, while surpassed 0.800 for brown rice rate, milled rice rate, head rice rate and chalky grain rate, and were 0.778 for transparency and 0.573 for amylose content. Validation results demonstrated that, except for amylose content, the test R² values for the other eight traits were all above 0.600, confirming satisfactory model fit. Thus, the proposed functional models accurately captured how each quality trait responds to changes in grain−filling temperature. With sowing dates delayed, the average grain−filling temperature ranged from 30.8℃ to 18.4℃. Processing quality traits, brown rice rate, milled rice rate and head rice rate, exhibited a trend of initial increase followed by decrease, with optimal T_ave of 22.0℃, 22.0℃and 25.3℃, respectively. Appearance quality traits, chalky grain rate, chalkiness degree and transparency, showed an opposite pattern, first decreasing and then increasing, with optimal T_ave of 25.7℃, 25.5℃and 25.2℃, respectively. Eating quality traits responded differently: the amylose content remained relatively stable, gel consistency decreased monotonically, and alkali spreading value increased monotonically. Both excessive high and low temperatures adversely affected processing and appearance quality. Higher temperatures favored a softer gel consistency but resulted in a higher gelatinization temperature, whereas lower temperatures favored a lower gelatinization temperature but resulted in a harder gel consistency. Comprehensively, a grain−filling T_ave of 25℃ yielded relatively optimal processing and appearance quality. The corresponding trait values were 80.7% for brown rice rate, 71.2% for milled rice rate, 55.1% for head rice rate, 12.7% for chalky grain rate, 3.1% for chalkiness degree, 1.7 for transparency, 17.9% for amylose content, 62.7mm for gel consistency and 5.2 for alkali spreading. These met the Grade 3 high−quality rice standards specified in the agricultural industry standard "Quality of Edible Rice Varieties" (NY/T 593−2021).

This study quantified the integrated climate risk posed by multiple hazards to double−cropping early rice in Nanhai district of Foshan, under climate change and identified the sowing period that minimize this risk. Based on annual observations of early rice from the Guangdong agrometeorological experiment station and the daily meteorological data of the Nanhai national meteorological station from 1981 to 2025, the sliding−window correlation analysis identified the critical exposure periods and response variables for the five hazards. K−Means clustering was used to define the threshold of each disaster level, frequency−based probability estimates and cumulative deviation were used to derive hazard and vulnerability indices, and a weighted expectation model was used to calculate an integrated climate risk index for each sowing period group. Robustness was evaluated under four vulnerability weighting scenarios. The results showed that: (1) the critical periods for cold damage, chilling injury, low sunshine, rainstorm and high temperature were sowing to transplanting, transplanting to tillering, 5d before heading to 15d after heading, 15d before heading to 20d after heading and 10d before heading to 20d after heading, respectively. Their corresponding response variables were the duration from sowing to transplanting, the duration from transplanting to tillering, actual yield, seed−setting rate and 1000−grain weight, respectively. (2) Historical sowing dates clustered into early (late February), intermediate (early March) and late (mid−March) groups by K−Means. As the early rice sowing period was delayed, the risk indices for cold damage, chilling injury and low light conditions showed a declining trend, with cold damage decreasing from 1.268 to 0.152, a reduction of 88.0%. In contrast, the high−temperature risk index increases significantly, rising from 1.044 to 1.231, an increase of 17.9%, while the heavy rainfall risk index changed relatively little. (3) The integrated climate risk index for the late−sown early rice group in Nanhai was 1.086, which was 2.25% and 17.29% lower than those of the mid−sown and early−sown groups, respectively. Under four vulnerability weighting scenarios, the ranking of the integrated climate risk indices for early rice across different sowing periods remained consistent, indicating strong robustness of the conclusions. Adjusting the optimal sowing period for early rice in Nanhai district of Foshan, to mid−March can effectively reduce the risk of low spring temperatures and minimize overall climate risk. The findings provide valuable references for optimizing early rice sowing schedules and climate adaptation decisions in similar regions.

Based on daily temperature data from 1990 to 2019 in the Xinjiang cotton region and cotton yield data for each county (city, district), the Mann−Kendall trend test and Sen’s slope method were used to analyze the spatiotemporal variation characteristics of cotton yield and low−temperature cold damage during the cotton growing season (including seedling stage and flowering−boll stage cold damage). The copula function was employed to assess the impact of low−temperature cold damage on cotton yield. This study aimed to reveal the historical evolution of both cotton yield and low−temperature cold damage, and to quantify the impact of low−temperature cold damage on the probability of yield reduction, helping cotton farmers mitigate production risks. The results showed that, from 1990 to 2019, cotton yield exhibited a highly significant increasing trend (P<0.01) in 86.3% of the Xinjiang region, with an average annual increase of 30.22 kg·ha⁻¹. The high−value cotton production areas were mainly concentrated near the Tianshan mountains, while the yield stability showed a gradual increase from west to east. During the period from 1990 to 2019, significant changes in low−temperature cold damage occurred in the cotton−growing regions of Xinjiang. In 27.4% of the cotton-producing counties (districts and cities), the number of cumulative days of low−temperature stress during the seedling stage (LST) showed a highly significant decrease. Moreover, 37.0% of the counties experienced a highly significant decreasing trend (P<0.01) in cumulative low−temperature stress days during the flowering−boll stage (LTB). As the cumulative number of LST days increased, the probability of cotton meteorological yield loss increased by 19.1 percentage points in southern Xinjiang and by 19.0 percentage points in northern Xinjiang. Similarly, as cumulative LTB days increased, the probability of yield reduction increased by 18.6 percentage points in southern Xinjiang and by 20.5 percentage points in northern Xinjiang. In conclusion, over the past 30 years, cotton yield in Xinjiang has significantly increased, the frequency of low−temperature cold damage has generally decreased, and as the duration of low−temperature cold damage increased, the probability of cotton yield reduction rose, with the impact being more significant in northern Xinjiang than in southern Xinjiang.

Based on the long−term homogeneous data of 17 national meteorological stations in the Hexi corridor from 1961 to 2024, this study analyzed spatiotemporal variation characteristics of effective accumulated temperature of ≥10℃, water heating value, sunshine hours and cumulative precipitation and temperature before harvest during the potential growing period of wine grapes using trend analysis, mutation test and change difference test. The impact of key meteorological factors on the climate quality of wine grapes was assessed to provide a scientific basis for adapting wine grape cultivation to climate change in the Hexi corridor. The results showed that thermal resources had significantly improved, effective accumulated temperature of ≥10℃ showed a significant upward trend (6.1℃·d·y⁻¹, P<0.01) from 1961 to 2024. There was a mutation around 1997, the average effective accumulated temperature during the potential growing period increased from 1169℃·d to 1408℃·d, and improved the climate quality of wine grapes from average to excellent level. Water heat value also showed a significant upward trend (11.6℃·mm·y⁻¹, P<0.01), and multiple thresholds were broken during the potential growth period of wine grapes, leading to adverse effects on the climate quality of wine grapes in Zhangye, Jinchang, Wuwei and other areas. Sunshine resources had become abundant and continuously optimized (2.7h·y⁻¹, P<0.01). The trend of increasing temperature and sunlight was particularly evident in low−altitude areas, which had a positive significance for the quality formation of wine grapes. The overall climate quality of wine grapes had improved in the Hexi corridor, with significant increases in the Jiuquan, Gaotai and Wuwei areas on the eastern side of the Qilian mountains. The area of suitable planting had expanded to the east, while there had been a slight decrease in the southwestern mountainous areas. The study had shown that climate change had a significant threshold effect and characteristics of regional differentiation on the climate quality of wine grapes. The increase in heat and light resources in the Hexi corridor were the dominant factors in improving the climate quality of wine grapes. However, excessive warming and an increase in water heat can also increase the risk of a decline in the climate quality of wine grapes.

 Based on a dataset of 177 samples of fruit set rate and meteorological factors obtained from experiments in Fujian from 2020 to 2024, a correlation analysis was used to screen for characteristic meteorological factors that significantly affect fruit set rate. Three machine learning algorithms, that of multiple stepwise regression (MSR), random forest (RF) and gradient boosting decision tree (GBDT), were employed to develop meteorological models for predicting the fruit set rate of yellow passion fruit. The study aimed to provide a scientific reference for optimizing cultivation management and enhancing the production of yellow passion fruit in Fujian. The results showed that: (1) the MSR model showed that only four out of 40 characteristic meteorological factors significantly indicated the fruit set rate of yellow passion fruit at the 0.05 significance level. Specifically, the higher average temperatures from 10d before flowering date to the flowering date, shorter sunshine duration during the 11−20d before flowering date, greater precipitation on the flowering date and longer sunshine duration on the flowering date could lead to a decreased fruit set rate of yellow passion fruit. (2) Analysis of the top eight most important meteorological factors among 40 characteristic meteorological factors for the RF and GBDT models revealed that the common determinant meteorological factors influencing the fruit set rate of yellow passion fruit were the maximum temperature from 10d before flowering date to the flowering date, the average temperature from 10d before flowering date to the flowering date, the precipitation on the flowering date, relative humidity on the 20d before flowering date, precipitation during the 11−20d before flowering date. (3) The root mean square error (RMSE) values of the MSR, RF and GBDT models on the test set were 16.7, 10.7 and 11.7 percentage points, respectively, while the determination coefficients (R²) were 0.66, 0.86 and 0.83, indicating favorable simulation performance across all three models. The MSR model demonstrated strong interpretability, although its prediction accuracy was significantly lower than that of the GBDT and RF models. The RF model achieved the best performance on the test set, exhibiting superior generalization capability. In contrast, the GBDT model showed the best fit on the training set (RMSE=6.8 percentage points, R²=0.95), yet its performance on the test set was slightly inferior to that of the RF model.

Based on the staged sowing test data of Guangdong Nanhai early rice 'Yefengzhan' from 2018 to 2021 and the daily meteorological data of the Nanhai national basic station, a Logistic growth model was used to simulate the dynamic changes in the total aboveground dry matter weight from early rice transplanting to maturity and the thousand−grain weight from heading to maturity. The modified Gompertz model was used to simulate the dynamic changes of early rice leaf area index from transplanting to the milky stage, and established the correlation path of meteorological factors−growth parameters−yield changes, in order to analyze the regulation of early rice growth parameters by changes in meteorological conditions at different growth stages of early rice and its impact on yield. The results showed that the Logistic model based on the aboveground dry matter weight from transplanting to maturity stage, leaf area index from transplantation to milk maturity stage, thousand−grain weight from 10d after heading to maturity stage and meteorological data from 2018 to 2021 for Nanhai early rice 'Yefengzhan', the fitting accuracy of simulating aboveground dry matter weight and thousand−grain weight of early rice and the modified Gompertz model simulating early rice leaf area index were both high, with goodness of fit R²>0.98 and normalized root mean square error NRMSE≤5.71%. A total of 11 early rice growth parameters were derived from the two model simulations, of which five parameters (maximum dry weight potential, dry weight accumulation inflection point, maximum leaf area index, maximum thousand−grain weight potential and grain filling inflection point) were significantly related to early rice yield and were determined as core parameters (P＜0.05). Correlation analysis between yield, core parameters and meteorological factors at different growth stages showed that there were two main physiological regulatory paths through differences in meteorological conditions drived changes in core parameters and thus affected early rice yield changes: one was the path of photosynthetic production and grain capacity building, which could be significantly improved by increasing the number of sunshine hours during the sowing−heading stages. The maximum leaf area index (LAI_max) and thousand−grain weight (G_max) led the foundation for the high yield of early rice. The second was the growth process and grain filling path of early rice. The number of high−temperature days during the tillering−heading stages significantly delayed the early rice filling inflection point (DAH_m), extending the effective grain filling period, which was conducive to an increase in grain weight. Precipitation and lack of sunshine during the sowing−jointing stage were the main stress factors that reduce the yield of early rice. In summary, ensuring sufficient light during the jointing−heading stages of early rice in the south China Sea and avoiding rainy weather during the sowing−jointing stages and high temperature stress during the grain filling stage are key to improving the climate adaptability of early rice in this region.

The intensification of global climate change, characterized by temperature anomalies, spatiotemporal variability in precipitation and secondary disasters, has significantly affected the yield, quality and genuineness of plant−derived medicinal materials, and seriously threatened the cultivation and management of medicinal herbs. This study reviewed the effects of extreme climate events on the growth and pharmacological component contents of botanical Chinese medicinal materials, and discussed the interfering impacts of agro−meteorological disasters such as high temperature stress, low−temperature freezing injury, drought and flood on the physiological and ecological processes of Chinese medicinal herbs. It further explored how these events drove habitat migration and market speculation in the medicinal herb industry, while proposing countermeasures centred on meteorological monitoring and early warning systems, innovative adaptive cultivation techniques and ecological compensation mechanisms. The results showed that extreme climate events, such as heat stress, freezing injury and abnormal precipitation, significantly reduced medicinal herb yields. Regional extreme weather also could diminish the content of active compounds like total phenols, flavonoids and saponins, leading to fluctuations in efficacy, price volatility and weakened market stability. Targeted climate adaptation measures should be formulated in response to extreme climate events. The MaxEnt model could be adopted to clarify and optimize the suitable planting areas of medicinal materials. Facility cultivation such as greenhouse and breeding could be appropriately carried out for meteorological sensitive varieties with protective management. Meanwhile, it is necessary to construct an integrated disease prevention and control system, and coordinate the collaborative mechanism of post−disaster emergency recovery and long−term ecological compensation. These measures can improve the climatic and market adaptability of Chinese medicinal materials, so as to alleviate the adverse impacts of extreme climate change on medicinal material production.

Based on the daily precipitation data from 172 national meteorological observation stations in the Huaihe river basin and the 500hPa ridge position data of the northwest Pacific subtropical high from 1960 to 2022, this study used the Empirical orthogonal function (EOF) to delineate the boundary between the northern and southern subregions of the basin. By using the sliding average and statistical test methods, a set of key indicators for the monitoring of the rainy season in the Huaihe river basin was established, including the determination of the number of rainy days in the region, the start and end dates of the rainy season in the region, and the rainy season in the whole Huaihe river basin. Based on the key indicator system, this study analyzed the rainy season characteristics of the entire Huaihe river basin from 1960 to 2022, and selected typical drought and flood years to verify the start and end dates of the rainy season, aiming to establish a scientific and practical monitoring method for the rainy season in Huaihe river basin. The results showed that the southern and northern subregions of the Huaihe river basin during the rainy season were bounded by the zero line of the spatial loading coefficient of the second EOF mode. Regional rainy days were identified when more than one-third of the meteorological observation stations in the region record at least 0.1mm of daily precipitation, and the average daily precipitation in the region reached or exceeded 1.0mm. From 1960 to 2022, the average onset date of the rainy season in the southern subregion was June 20, the average end date was July 20, and the average duration was 31d. In the northern subregion, the average onset date was June 25, the average end date was July 25, and the average duration was 29d. For the entire Huaihe river basin, the average onset date was June 19, the average end date was July 26, and the average duration was 32d. To verify the reliability of the indicator system, a typical drought year (1988) and a typical flood year (1991) were selected to conduct a comparative analysis between their rainy season start−end characteristics and the actual meteorological disaster features. In 1988, the Huaihe river basin was identified as having no rainy season, in line with the characteristics of the basinwide drought disaster that year. In 1991, the rainy season in the Huaihe river basin began on May 29 and ended on August 7. Compared to the average rainy season (June 19–July 26) during 1960–2022, it started earlier and lasted longer. This aligns with the severe, widespread and persistent basin−wide flooding of that year, thereby confirming the reliability of the rainy season monitoring indicators for the Huaihe river basin established in this study. The monitoring indicators for the rainy season established in this study provide a theoretical basis and technical support for meteorological services related to agricultural production, flood control and drought mitigation in the Huaihe river basin. They are also highly significant for improving the early warning of meteorological disasters and the scheduling capabilities of resources within the basin.