Trends and Lagged Cumulative Associations Between Vegetation NDVI and Extreme Climate Indices in the Yangtze River Basin: A 41-Year Observational Analysis

Abstract

Extreme climate events can disturb vegetation dynamics and alter ecosystem stability. This study investigated the evolution of extreme climatic conditions and vegetation greenness across the Yangtze River Basin during 1982–2022 by integrating CN05.1 daily meteorological records with the PKU-GIMMS NDVI V1.2 dataset. Using the index framework of the ETCCDI, fifteen extreme weather-related climate indices were calculated. Trend magnitudes were quantified using Sen’s slope estimation, while the Mann–Kendall trend test was used to determine the direction and significance of long-term changes. Both standard correlation and lagged cumulative correlation were used to assess NDVI–climate relationships; the latter tested ten predefined windows formed from climate conditions between the current month and three months before each NDVI observation, with the largest absolute correlation defining the optimal window. The basin experienced a distinct warming signal in temperature extremes during 1982–2022, characterized by reduced occurrences of TN10p and TX10p, together with more frequent TN90p and TX90p events. Precipitation extremes showed an overall intensification, with R95p and R99p increasing by 9.2 mm per decade and 5.6 mm per decade. NDVI also increased throughout the analysis period, with a basin-wide rate of 0.0038 per decade and the fastest greening in the middle reaches at 0.0069 per decade. Vegetation NDVI was generally positively associated with warm-related temperature indices and negatively associated with cold-related indices. The lagged cumulative analysis identified the strongest NDVI–climate associations within windows combining current and antecedent climate conditions, with marked differences among subregions and vegetation types. Because the analyses are correlation-based and did not quantify non-climatic drivers such as land-use change or ecological restoration, the results should be interpreted as statistical associations rather than causal attribution. Overall, these findings characterize how vegetation greenness covaries with extreme climate indices across the Yangtze River Basin.