Phenology refers to the periodic natural phenomena of animals, plants, weather, etc., that change with seasons. The Seventy−two pentads are the result of the meticulous observation and summary of natural phenomena by ancient ancestors in the practice of long−term production, and their application to the judging of agricultural time. This study built on previous studies and focused on the formation, evolution, characteristics and implications of the Seventy−two periods. It analyzed the record of the Seventy−two periods in historical literature and summarized their economic, social and cultural values in conjunction with contemporary production and life. The results showed that the historical changes of the Seventy−two pentads went through a long process from sporadic to abundant, from scattered to orderly, and from folk to official. Its response system had significant characteristics such as naturalness, timing, correspondence, typicality and dynamism, in accordance with the laws of natural science and agricultural production. The combination of promoting the Seventy−two pentads and modern technological means can provide more accurate and efficient guidance and services for agricultural production.

This study examined the rhythmic patterns and temporal shifts in flowering phenology of 48 plant species in Beijing by analyzing observed phenological and meteorological data from 1963 to 2012. Focusing on six key flowering stages and utilizing the Twenty-four solar terms as a temporal framework, authors applied linear trend estimation, correlation analysis, and statistical testing to investigate phenological characteristics, changing trends, and characteristics of climate response. The results showed that: (1) nearly 80% of the species exhibited advancing trends in their flowering phenology, with over half demonstrated significant shifts of 5-25 days earlier in their first, peak and last flowering date (P<0.05). (2) Successive flowering stages for over 70% of species occurred within the same or adjacent solar term. Flowering was most concentrated during spring and summer: flower bud swelling and opening peaked at Hibernator Awakens (HA, 71.8%) and Spring Equinox (SE, 73.9%), inflorescence or flower bud emergence was most frequent during SE and Fresh Green (FG, 58.4%); first and peak flowering dates were most common from FG (54.2%) and Grain Rain (GR, 56.3%); and last flowering dates clustered between GR and Summer Begins (SUB, 54.1%). (3) Flowering duration increased lengthened for 60.4% of species (mean+8d) but shortened for 39.6% (mean-3d). The extension was primarily attributed to an earlier first flowering date (FFD, 43.8%), while shortening mostly was mainly due to an earlier last flowering date (LFD, 33.3%). (4) The average flowering duration across all species ranged from 5 to 80d. Spring-flowering species generally had shorter durations than summer-flowering ones, with 77.1% of spring bloomers completing their flowering within a 15-day period (equivalent to one solar term). (5) 91.8% of the statistical series of the average temperature between the FFD of 48 plant species and the Minor Cold (MIC) to Minor Heat (MIH) showed a negative correlation, with the most pronounced correlations observed during HA, SE and FG (each showing a negative correlation proportion of 95.8%). Spring-flowering species exhibited higher temperature sensitivity than summer-flowering species. Over 90% of spring-flowering species showed a significantly negative correlation (P<0.05) between their FFD and mean temperature during HA, SE and FG. Furthermore, the first flowering date was significant negatively correlated (P < 0.05) with the mean temperature of its average occurrence solar term and the four preceding solar terms. Among these, early-spring flowering species were the most temperature-sensitive, followed by late-spring and summer species. By adopting the perspective of solar terms, this research elucidates the rhythmic patterns of flowering phenology in Beijing, its shifts under climate warming, and its strong temperature dependence. These findings underscore the sensitivity of plant phenology to climate change and provide a scientific basis for adaptive plant management, flowering forecast, and the planning of floral tourism activities within the framework of solar terms.

Based on the daily observed meteorological data from 585 meteorological stations in mainland China from 1961 to 2020, this study analyzed the spatiotemporal distribution and variability characteristics of the temperature on the twenty−four solar term scale. The interdecadal differences in temperature within solar terms were also clarified, as well as the changes in the onset and duration of climatological seasons under the background of climate change. The results showed that the highest temperature occurred during the Lesser Heat and Greater Heat solar terms in mainland China from 1961 to 2020, which was 24.1℃ and 24.4℃, respectively. A noticeable coolness appears in the North China Plain during the White Dew and Autumnal Equinox solar terms, marking the onset of the climatological autumn. The Lesser Cold and Great Cold solar terms showed the lowest average temperature with the mean value of −2.8℃ and −2.5℃, respectively. The average temperature of the whole country showed a significant change characteristic of stronger in north than south of China, with drastic changes in spring and autumn during the transition of solar terms. The Pure Brightness−Grain Rain and Awakening of Insects−Spring Equinox solar terms exhibited the highest temperature increases (3.6℃and 3.4℃, respectively), while the Beginning of Winter−Lesser Snow solar term showed the largest temperature decrease (−3.9℃). The temperature changes during spring and autumn solar terms were significantly more pronounced in Northeast and North China compared to other regions. Under the background of climate change, the isotherms of the threshold temperatures (0℃, 10℃and 22℃) have shifted northward, while the duration of climatological summer becoming longer and winter shorter. These changes were more pronounced in northern regions compared to southern regions. Compared to the period from 1961−1990 (period I), the average temperature increase during winter and spring solar terms in 1991−2020 (period II )(0.6−1.9℃) was greater than that in summer and autumn solar terms (0.5−0.9℃), and the Rain Water solar term showed the largest increase. The changes in the duration of climatological seasons were most significant in Northeast, North and Northwest China, where summer duration increased by 6.7−8.6 days, and winter duration decreased by 7.1−9.3 days. The findings provide scientific evidence for optimizing regional agricultural climate resource allocation and formulating differentiated agricultural adaptation strategies to enhance climate resilience under the changing climate. 

Based on the rice field experiment data from the Jiangjin National Agricultural Meteorological Experimental Station in Chongqing in 2020, authors constructed and utilized a locally calibrated DSSAT−CERES− Rice model to simulate the impact of different high temperature on the growth and development of rice during its key growth stages, as well as yield. In addition, the appropriate sowing date for 'Yixiangyou 2115' rice based on the Twenty−four solar terms scale was explored, providing a reference for achieving high and stable yield in the Sichuan basin. The results showed that: (1) the simulated values of the DSSAT−CERES−Rice model, calibrated with local parameters, exhibit a significant correlation with the observed values. The coefficients of determination for the model's simulated rice flowering date, maturity date and final yield, compared to the actual observed values, were 0.97, 0.94 and 0.70, respectively, while the root mean square errors were 0.95d, 1.35d and 248.06kg·ha⁻¹, respectively. (2) When rice was only subjected to high temperature stress (daily maximum temperature ≥35℃ for 3d or more consecutive days) during the heading−flowering stage, the yield reduction rate ranged from 4.3% to 6.8%, which was significantly higher than the yield reduction rate (<2.2%), when rice was subjected to high temperature stress during the grain filling−milky stage. This indicated that rice during the heading−flowering stage was more sensitive to high temperatures, and the higher temperature and the longer duration, the higher yield reduction rate of rice. When both the heading−flowering and grain filling−milky stages were subjected to compound high-temperature stress with a daily maximum temperature of 39℃ lasting 7d at each stage, the yield reduction reached 8.2%, increasing by 1.7 and 7.5 percentage points compared with single−stage stress during the heading−flowering and grain filling−milky stages, respectively. (3) The simulation results of the locally parameterized DSSAT−CERES−Rice model for rice yields at different sowing dates showed that the optimal sowing date for 'Yixiangyou 2115' in Jiangjin of Chongqing, was on the day of the Hibernator Awakening.

Based on daily mean temperature observations from 18 meteorological stations in Renhuai, from 2014 to 2023 and Digital Elevation Model (DEM) data, 1km×1km daily mean temperature grid dataset was constructed by Light gradient boosting machine (LightGBM) to impute missing station data and Random forest regression kriging (RFK) spatial interpolation. The spatio−temporal evolution characteristics of mean temperature at the Twenty−four solar terms scale (solar−term mean temperature) was analyzed, while its relationship with the timing of sauce−aroma baijiu production processes, aiming to clarify the characteristics of solar−term mean temperature changes in Renhuai under the background of climate warming, thereby providing scientific reference for optimizing local sauce−aroma baijiu brewing technology and enhancing climate adaptation. The results showed that: (1) the LightGBM algorithm demonstrated high reliability in imputing missing station data and effectively captured the distribution characteristics and variation processes of the true values. The gridded temperature data interpolated using the RFK algorithm exhibited superior applicability and stability in the topographically complex terrain of Renhuai. (2) From 2014 to 2023, the annual and four seasonal−type solar terms mean temperatures in Renhuai exhibited an overall spatial pattern of "warmer river valleys and cooler mountainous areas". While the Chishui river valley had higher solar−term mean temperatures than the hill−valley transition zone with smaller interannual variability. Among the Twenty−four solar terms, the highest average temperature occurred during Major Heat (26.3℃), and the lowest during Minor Cold (5.3℃). Spring−type solar terms showed an overall warming trend. The earliest solar term reaching the phenological threshold of spring−type mean temperature advanced from the Spring Equinox to Hibernator Awakening, while the latest solar term departing from the summer−type mean temperature phenological threshold was delayed from Heat Withdraws to White Dew. This indicated that the phenology in Renhuai exhibited "a warming feature of earlier warming and delayed cooling". (3) At Maotai station, the days with daily mean temperatures between 20−30℃ accounted for 37.7% of the year, providing suitable thermal conditions for Daqu fermentation in sauce−aroma baijiu production. The timing of the seven-rounds was highly synchronized with the temperature changes across the Twenty−four solar terms: relatively lower daily mean temperatures in the early stages favored the accumulation of flavor compounds, while rising temperatures in the later stages promoted an increase in alcohol content in the liquor body.

Based on the data such as daily maximum temperature, precipitation from 20:00 to 08:00 the next day and from 08:00 to 20:00, air relative humidity at 14:00, and wind speed at 14:00 observed at 17 national surface meteorological stations in Shijiazhuang from May 20 to June 15 during 1972−2025, this study analyzed the temporal and spatial variation characteristics of dry−hot wind using statistical methods, and it also clarified the correlation between temperature, humidity and wind speed on annual typical dry−hot wind day. The results showed that: (1) the number of dry−hot wind days in Shijiazhuang increased at rates of 0.6d·10y⁻¹ from 1972 to 2025 and 2.1d·10y⁻¹ from 2000 to 2025. The decadal average of dry-hot wind days was the lowest in the 1990s (8.4d·y⁻¹) and the highest in the 2020s (14.8d·y⁻¹). Moderate dry−hot wind days from 1972 to 2025 and severe dry−hot wind days from 2000 to 2025 showed a significant increasing trend (P<0.05). (2) Among the 17 stations in Shijiazhuang from 1972 to 2025, moderate dry−hot wind events occurred with the highest frequency, mainly concentrated from June 3 to 15. Moderate dry−hot winds occurred frequently throughout the first and second pentads of Harvesting and Sowing, and its days increased year by year in the second and third pentads of Approaching Fullness. (3) Abrupt changes were detected in the total number of dry−hot wind days as well as the days of mild, moderate and severe dry−hot winds in Shijiazhuang from 1972 to 2025. The most recent abrupt change relative to 2025 was an abrupt increase for all categories: mild dry−hot wind days increased abruptly in the late 1980s, while the others increased abruptly after 2016. (4) The decadal average number of total dry−hot wind days and that of mild, moderate and severe dry−hot wind days at the 17 stations in Shijiazhuang from 1972 to 2025 ranged from 4.7 to 8.3, 0.6 to 1.4, 2.4 to 4.3 and 1.1 to 2.6d·y⁻¹, respectively. All showed high−value areas in the southwestern parts of the study region, and the spatial distribution of decadal average moderate dry−hot wind days was basically consistent with that of the decadal average total dry−hot wind days. (5) Annual typical dry−hot wind days in Shijiazhuang from 1972 to 2025 mainly occurred from June 4 to 15 (accounting for 75.9%), with the highest frequency in the first pentad of Harvesting and Sowing, followed by the second pentad of Harvesting and Sowing, and the distribution of affected stations showed regional characteristics (affecting no less than 9 stations per day). On these days, the relative humidity at 14:00 was negatively correlated with both the daily maximum temperature and the wind speed at 14:00, and the correlation between the relative humidity at 14:00 and daily maximum temperature passed the significance test (P<0.05). The results of this study can provide scientific guidance for the management decision−making of winter wheat in Shijiazhuang during the grain−filling and maturation stages, as well as provide meteorological support to ensure high grain yield and increase farmers′ income.

In response to the challenges posed by climate change, the No.1 Central Document for the period 2022–2026 has on four occasions outlined the requirements for meteorological services in agriculture with respect to medium– to long–term climate change impacts. The requirements include strengthening research on the impact of climate change on agriculture, initiating a new round of surveys and zoning of agricultural climate resources, enhancing short–term early warning and medium– to long–term trend assessment of meteorological disasters, and conducting surveys and regionalization of agricultural climate resources. To strengthen the technological support for climate services in agriculture, this study systematically analyzed the practical context, scientific connotations and major technological challenges associated with the No.1 Central Document from 2022 to 2026. The aim was to provide scientific and technical references for implementation of the document's directive on climate services for agriculture. The results showed that the meteorological service content in the No.1 Central Document from 2022 to 2026 reinforced the demands for climate services in agriculture, forming three core tasks: strengthening research on medium– to long–term climate change impacts on agriculture, improving medium– to long–term trend assessments of meteorological disasters, and conducting surveys and regionalization of agricultural climate resources. Together, these tasks established a scientific collaboration chain of climate change impact and adaptation, framed as "risk perception–prevention and control decision–making–adaptation layout". However, there were currently three major bottlenecks in terms of technological support for achieving this task: first, the scientific understanding of the impacts of climate change on agriculture was still insufficient in terms of attribution, extreme event impact assessment, and full–chain effects. Second, medium– to long–term agricultural meteorological disaster prediction techniques require breakthroughs in prediction accuracy, mechanism constraints and indicator applicability. Third, adaptation of crop layouts to climate change lacks the dynamic, precise and multidimensional support of agricultural climate zoning. In the future, there is an urgent need to advance scientific and technological research focused on these bottlenecks to effectively transform climate information into actionable decision–making support for agriculture, so as to genuinely serve the national strategies for food security and sustainable agricultural development. 

Accurately simulated evapotranspiration (ET) is important for evaluating the effect of plantation on water cycle in the planting area, and promotes the optimization and management of local water resource. This study focused on the Pinus sylvestris var. mongolica plantations in western Liaoning province. Evapotranspiration measured by eddy covariance system (ET_EC) during the growing season in 2023 and 2024 was used to evaluate the applicability of the Penman−Monteith (PM) model, Shuttleworth−Wallace (SW) model and the modified Priestley−Taylor (PT_m) model, and analyzed the sensitivity of the important variables in the three models. The results showed that the three models estimated evapotranspiration (ET_e) had similar diurnal and seasonal variations to ET_EC. However, during the growing seasons of 2023 and 2024, the PM model underestimated by 39.24% and 27.05%, the PT_m model underestimated by 20.85% and 4.75%, while the SW model underestimated by 1.12% and overestimated by 5.12%. At half-hour and daily time scales, the SW model had the smallest mean absolute error (MAE) and root mean square error (RMSE), as well as the highest simulation efficiency (ME) and consistency index (IA) during the two growing seasons. At daily time scale, the MAE, RMSE, ME and IA obtained by the PT_m model were in the middle of three models during the two growing seasons. For the resistance variables of the PM model and SW model, ET_e was the most sensitive to canopy resistance (r_c). For the environmental variables of the three models, ET_e was the most sensitive to root zone soil moisture content (θ_r). Both r_c and θ_r had greater impact on ET_e in the drought year of 2023. The SW model was the most suitable for simulating ET in the Pinus sylvestris var. mongolica plantation in western Liaoning province. Future studies should measure the ET components and verify the performance of the model in the ET partitioning. 

To objectively evaluate the impact of climate change on the climatic suitability of liquor−making sorghum in northern Guizhou and provide a scientific basis for optimizing planting layouts, this study analyzed the spatiotemporal evolution of climate suitability based on daily meteorological data from 14 stations in the main sorghum−producing areas from 1961 to 2024. By combining the sorghum growth indices, suitability models were constructed for temperature, moisture, sunshine and comprehensive climate suitability. The results showed that: (1) the temperature suitability for the growth stage exhibited a significant upward trend, and both the  growth stage and the five individual growth stages were classified as suitable from 1961 to 2024. (2) Moisture suitability during the sowing, seedling and jointing−booting stages exceeded 0.80 with high stability. However, the flowering−grain filling and maturity stages exhibited significant interannual fluctuations, identifying late stage water management as key to stable yields. (3) Sunshine suitability was moderately suitable for the growth stage and early stages, but suitable for the flowering−grain filling and maturity stages. A significant downward trend in sunshine suitability was observed during the jointing−booting stage and throughout the growth stage. (4) The comprehensive climate suitability ranged from 0.71 to 0.89 (suitable level) with no significant downward trend. (5) Approximately 93% of the region was suitable for liquor−making sorghum, primarily below 1350m. High−suitability zones (>0.76) were concentrated between 500m and 1250m in the western, central−southern, eastern and some townships of the central−northern Guizhou. In summary, northern Guizhou is generally suitable for liquor−making sorghum, despite the presence of phasic water stress. Optimizing the planting layout and enhangcing water management during the late growth stage are recommended to enhance climate resource utilization and industrial resilience.

This study used the different cotton growth date and meteorological data from 22 agricultural meteorological stations in different cotton regions of Xinjiang from 2000 to 2023. The daily variation of temperature and the warming effect of plastic mulch had been introduced to improve the conventional climatic suitability method by incorporating indicators of temperature development at different growth stages of cotton. A prediction model of cotton growth stages applicable to different cotton regions of Xinjiang has been constructed. The mean absolute error (MAE), root mean square error (RMSE) and normalized root mean square error (NRMSE) were used to evaluate the improved model. The aim was to address the large simulation bias in conventional climatic suitability method, which resulted from neglecting the daily variation of temperature and the warming effect of plastic mulching, and thereby to improve the accuracy of cotton growth stage prediction in Xinjiang. The results showed that: (1) from 2000 to 2023, the sowing date and emergence date of cotton in Xinjiang cotton region showed advanced trends, which tendency rates were −2.16d·10y⁻¹ and −1.44d·10y⁻¹ in northern Xinjiang, and −0.47d·10y⁻¹ and −0.96d·10y⁻¹ in southern Xinjiang, respectively. However, the boll opening date showed a delayed trend with tendency rates of 1.76d·10y⁻¹ and 0.36d·10y⁻¹ in northern Xinjiang and southern Xinjiang, respectively. Budding and flowering date were advanced in northern Xinjiang, but delayed in southern Xinjiang. (2) From 2000 to 2023, in northern Xinjiang the main growth stages (sowing to emergence, emergence to squaring, squaring to anthesis, anthesis to boll opening) as well as the overall sowing to boll opening were prolonged (P<0.05). In southern Xinjiang, only emergence to squaring (P<0.01) and sowing to boll opening were extended, while other growth stages showed insignificant shortening trends (P>0.05). (3) From 2000 to 2020, based on the unimproved climatic suitability model, the MAE, RMSE and NRMSE of cotton at different growth stages from 2000 to 2020 were 2.0−24.6d, 2.7−30.1d and 15.7%−32.3%,respectively. After the introduction of the daily variation of temperature and the warming effect of plastic film mulching, the improved model significantly reduced errors to 1.9−9.6d (MAE), 2.6−11.8d (RMSE) and 6.4%−23.0% (NRMSE). (4) From 2021 to 2023, compared with the unimproved climatic suitability model, the improved MAE and RMSE of cotton growth stage were reduced by 4.2%−79.3% and 3.2%−82.2%, respectively. The accuracy of anthesis to boll opening and sowing to boll opening reached an excellent level (NRMSE≤10.0%), while other growth stages were at a good level (10.0 %<NRMSE≤20.0%). These findings demonstrate that the improved climatic suitability model can improve the simulation accuracy and stability of cotton growth stages in Xinjiang, with good regional adaptability and practical application prospects. 

Based on the daily meteorological data from 83 weather stations in Guizhou province from 1961 to 2025 and the ratio of tea polyphenol content to amino acid content (TP/AA) of spring tea collected from seven sampling sites (Meitan, Huaxi, Fenggang, Tongzi, Daozhen, Zheng'an and Duyun) for the years 2013, 2016, 2018, 2021 and 2023, this study employed methodologies including fuzzy mathematics, stepwise regression and Gaussian mixture models. Seven pre−harvest periods (3d, 5d, 7d, 9d, 11d, 13d and 15d prior to harvesting) were established. The average climate suitability index for each period was calculated. Key factors affecting TP/AA were screened by correlation analysis, enabling the simulation of spring tea quality and climate quality assessment. The results showed that the climate suitability for the spring tea season in Guizhou province from 1961 to 2024 ranged from 0.62 to 0.81. Overall, there was an increasing trend over time, with 93% of stations showing an increasing trend and 37% of stations showing a significant increase (P<0.05). The spatial distribution showed a decreasing trend from south to north and from east to west in Guizhou. Average climate suitability 15d before harvesting was identified as the major factor affecting the phenol to amino acid ratio. By further dividing the 15-day pre-harvest period into five consecutive sub-periods, a TP/AA simulation model was jointly constructed, which demonstrated good simulation accuracy for both the training and test sets (P=0.009). Based on the comprehensive climate suitability index and simulated phenol to amino acid ratio, Gaussian mixture clustering was employed to establish thresholds and grades for evaluating the climatic quality of spring tea: comprehensive index＞0.75 or simulated ratio≤4.98 as superior, 0.67<comprehensive index≤0.75 or 4.98<simulated ratio≤5.78 as excellent, 0.56<comprehensive index≤0.67 or 5.78＜simulated ratio≤6.40 as good and comprehensive index≤0.56 or simulated ratio＞6.40 as common. These results provide a scientific basis for climate−based quality evaluation and harvest timing regulation of spring tea.

This study evaluated the impactd of climate scenarios and land−use changes on the potential suitable habitats of Schisandra chinensis (Turcz.) Baill. based on land−use data and 55 actual species distribution records. The assessment employed the MaxEnt model under four climate scenarios (SSP126, SSP245, SSP370 and SSP585) for the present period (1970−2000) and three future periods (2041−2060, 2061−2080 and 2081−2100). The analysis quantified the driving mechanisms of key environmental factors and projected the dynamics of suitable habitats. The results are intended to provide data support for formulating conservation strategies for wild magnolia vine resources, guiding standardized ecological cultivation, and facilitating scientific regional planning. Furthermore, this research offers a reference for maintaining the survival and expansion of wild populations and for harmonizing the utilization of medicinal plant resources with ecological conservation. The results showed that: (1) the dominant factors affecting the distribution of suitable habitats of S. chinensis in China were precipitation of the wettest quarter, elevation, precipitation seasonality (Coefficient of variation), temperature seasonality (standard deviation), average maximum temperature in December, average monthly precipitation for May, slope and aspect, with a cumulative contribution rate of 91.6% to species distribution. (2) Under the historical climatic conditions, the highly suitable habitats of S. chinensis were mainly concentrated in the three northeastern provinces (Heilongjiang, Jilin, Liaoning), the junction of Hebei and Shanxi, the border area of Shaanxi, Gansu and Sichuan, and the mountainous area of central Shandong. In contrast to the historical period, the potential suitable habitats of S. chinensis exhibited an overall northward expansion trend under future climate scenarios (2041–2100): the newly emerging suitable habitats in northeast China expanded significantly, while the highly suitable habitats at the southwestern edge of the Shaanxi−Gansu−Sichuan border area shrank consistently. The centroid of suitable habitats generally shifted toward high latitudes, with the migration amplitude gradually increasing with rising emission intensity. (3)When superimposed with land use changes, under the SSP585 scenario during 2081−2100, the area of highly suitable habitats in croplands increased by approximately 319% compared with the historical period, and that in forest lands increased by about 184.3%. Overall, under future climate scenarios, land use change will drive the northward expansion and migration of suitable habitats for S. chinensis. It is recommended to prioritize the establishment of protected areas in vulnerable highly suitable habitats such as the Shaanxi−Gansu−Sichuan border region, promote forest ecological cultivation in the emerging suitable areas of northeast China while coordinating cropland protection, and establish a dynamic monitoring system to formulate differentiated management schemes, so as to achieve the conservation and sustainable utilization of its resources.

Based on the phenological observation data of pear blossom flowering periods and meteorological data in Chengdu Lihuaxi scenic area from 1999 to 2024, this study clarified the variation characteristics of the pear blossom flowering period (from the first flowering to the end flowering). It used Pearson correlation analysis to screen out the main meteorological factors affecting the first flowering. Based on these factors, a simulation model for the first flowering was developed by using stepwise regression. The period of the pear blossoms was defined as the optimal flower−viewing period. Combining human comfort conditions, precipitation conditions and wind conditions, a meteorological index for the flower appreciation of pear blossom in Lihuaxi scenic area was constructed. The aim was to provide scientific meteorological support for public flower appreciation activities and scenic area management. The results showed that: (1) the flowering period of pear blossoms in Chengdu Lihuaxi scenic area spaned from March 1 to April 10, with no significant trend in temporal change from 1999 to 2024. (2) Temperature was the key meteorological factor affecting the first flowering date of pear blossoms in Chengdu Lihuaxi scenic area. The first flowering was negatively correlated with the temperature starting from mid January and the effective accumulated temperature of ≥0℃, ≥5℃ and ≥10℃ in the previous winter. A prediction model for the first flowering of pear blossoms in Chengdu Lihuaxi scenic area was established via stepwise regression, and the factors composing the model included the effective accumulated temperature of ≥0℃, the average maximum temperature in mid January, the average minimum temperature in mid February. Validation showed that in 80% of cases the predicted first flower date differed from the observed date by less than 5d, with a mean absolute error of 3.4d. The simulation result was relatively satisfactory (P<0.01). (3) The constructed meteorological index of flower appreciation in Chengdu Lihuaxi scenic area was categorized into 4 levels: very suitable, suitable, moderately suitable and unsuitable for flower viewing. Specifically, the index was rated as very suitable when the blooming rate of pear blossoms was the highest (stable above 50%) and the tourist meteorological index was very good (the most suitable for human comfort with no daytime precipitation and strong wind). The index was suitable when the blooming rate was relatively high (gradually opened to 50% form first flowering or decreased from 50% until most flowers fallen off) and the tourist meteorological index was very good, or when the blooming rate was the highest and the tourist meteorological index was good (sub−suitable for human comfort with no daytime precipitation and strong wind). The index was moderately suitable when the blooming rate was relatively high and the tourist meteorological index was general (the most suitable or sub−suitable for human comfort with light rain and no strong wind during daytime). The index was unsuitable when the tourist meteorological index was bad (unsuitable for human comfort, moderate rain or heavier or strong wind during daytime). Combining the flowering period and tourism meteorological conditions, this index can provide guidance for the public to appreciate flowers and has favorable application value and potential in the subsequent tourism meteorological services.