Influenced by climate change, significant alterations in vegetation phenology have been observed globally. Grassland phenology is highly sensitive to climate change. However, research on the variations in grassland phenology and its responses to seasonal climatic changes in arid and semi-arid regions remains scarce. This study, utilizing Solar-Induced Chlorophyll Fluorescence (SIF) data, meteorological station data, and grassland type data, employs trend analysis and time series analysis to explore the trends of seasonal climatic variability and the sensitivity response of grassland phenology in Xinjiang to seasonal climates. The findings reveal the following: (1) The region experiences more pronounced warming in winter and spring than in summer and autumn, with ground temperature increments outpacing those of air temperatures. The summer season registers the peak in precipitation volume and rate of increase, where mountainous zones accrue more rainfall compared to basins and plains. The distribution of sunshine duration is characterized by higher values in eastern areas than in the west and more in the plains than in mountainous regions, potentially due to escalating cloudiness, which has contributed to a diminishing trend in sunshine hours across Xinjiang over the past 20 years. (2) Over the past two decades, the perennial greening phase of Xinjiang grasslands has predominantly occurred in early May, showing an overall trend of occurring earlier by approximately 5.47 days per decade, while the yellowing phase mainly occurs at the end of September and the beginning of October, demonstrating a delaying trend (6.61 days/decade). The average length of the growing season is 145 days, generally showing a slightly increasing trend (11.97 days/decade). (3) In spring, the rise in air and ground temperatures, along with increased sunshine duration, all promote grassland growth, leading to an earlier greening phase. Conversely, in autumn, increases in air temperature, ground temperature, and sunshine duration can inhibit grassland growth, resulting in an earlier yellowing phase. Increased precipitation in summer and autumn can delay the yellowing phase and extend the length of the grassland growing season. This research provides new insights into the factors influencing large-scale grassland phenology and offers references for grassland adaptation to future climate changes.

The Tarim Basin is located in an arid inland area; the ecological environment is fragile, and it is extremely sensitive to climate change. For the purpose of studying dynamic changes in the vegetation response of vegetation in the Tarim Basin to extreme climate, this study used the Vegetation Ecological Quality Index (EQI) as a vegetation indicator and calculated 12 extreme climate indices using Rclimdex. Pearson correlation analysis was used to explore the relationship between EQI values and various extreme climate indices at both inter-annual and intra-annual scales. Additionally, geographic detector analysis was employed to examine the single and interactive effects of extreme climate on the EQI for different vegetation types. The following was found: (1) During 2000–2022, the EQI showed an upward trend in the Tarim Basin, and the increase in agricultural vegetation was the fastest. (2) Since 2000, the extreme warm temperature indices have risen, whereas the extreme cold temperature indices have declined. The warming rate of nighttime temperatures exceeds that of daytime, and the extreme precipitation rises intensively. Simultaneously, continuous dry days have also increased. (3) On an inter-annual scale, the EQI is primarily negatively correlated with the most extreme warm temperature indices, while it is positively correlated with extreme cold temperatures and extreme precipitation indices. On an intra-annual scale, there is an obvious regional concentration in the correlation between the EQI and extreme climate indices. The diurnal temperature range (DTR) and cold daytimes (TX10P) have inhibitory and promoting effects on areas with high and low EQI, respectively. The extremum indices, temperature warm indices, and precipitation intensity indices have a promoting effect on areas with a high EQI and an inhibiting effect on areas with a low EQI. The interaction between extreme climate indices has a greater impact on the EQI than the effect of a single extreme climate index, especially with a significant impact on forests and shrubs. This study provides a reference for the early warning of meteorological disasters, ecosystem protection, and sustainable management in the Tarim Basin.

Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable areas such as coastal regions, subsequently influencing the dynamics of vegetation growth. However, there is limited research investigating the response of vegetation in these regions to extreme climates and the associated time lag-accumulation relationships. This study utilized a combined approach of gradual and abrupt analysis to examine the spatiotemporal patterns of vegetation dynamics in the coastal provinces of China from 2000 to 2019. Additionally, we evaluated the time lag-accumulation response of vegetation to extreme climate events. The results showed that (1) extreme high temperatures and extreme precipitation had increased over the past two decades, with greater warming observed in high latitudes and concentrated precipitation increases in water-rich southern regions; (2) both gradual and abrupt analyses indicate significant vegetation improvement in coastal provinces; (3) significant lag-accumulation relationships were observed between vegetation and extreme climate in the coastal regions of China, and the time-accumulation effects were stronger than the time lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2–3 months. These findings are important for predicting the growth trend of coastal vegetation, understanding environmental changes, and anticipating ecosystem evolution.