Dataset of Tree Rings and Growth Characteristics of Four Ancient Cypress Populations in the Alpine Climate Zone of Western Xizang (1855–2024)

Tree core samples were collected from 64 healthy ancient trees at four sampling sites in Ngamring County, Xigaze, Western Xizang using professional increment borers. Meanwhile, annual meteorological data from 1977 to 2020 were acquired from Lhatse National Meteorological Station. In the laboratory, tree-ring widths were precisely measured with high-precision dendrometers.

The COFECHA program, a widely adopted tool in dendrochronology, was used for cross-dating and data quality control to establish the regional standard tree-ring chronology from 1855 to 2024. For post-processing, multiple standard statistical methods were applied, including correlation analysis, kernel density estimation, assessment of tree responses to extreme events and growth synchrony analysis. These approaches facilitated the analysis of relationships between tree-ring indices and monthly climatic factors, statistics on tree age distribution, characterization of individual tree-ring width distribution, evaluation of resistance and resilience of ancient trees against extreme climatic events, and long-term tree growth asynchrony analysis, ultimately forming a complete and standardized dataset.Spatially, the study area is situated in the alpine climate zone of Western Xizang.

The four sampling sites in Ngamring County range from 86°45′13″ E to 87°09′08″ and 29°08′00″ N to 29°15′51″. Meteorological data were obtained from Lhatse National Meteorological Station (87°36′00″ E, 29°04′48″), with accurate spatial positioning that meets the requirements of regional climate and ecological research. Temporally, the core tree-ring chronology covers the period 1855–2024 with an annual resolution.

The supporting meteorological data span 1977–2020 at an annual resolution. The correlation analysis between tree rings and climatic factors adopts a monthly time scale for the period 1977–2020, enabling multi-temporal research on tree growth and climate responses.Stored in standard Excel format, the file consists of eight independent worksheets with clear structures and classifications:1. Distribution of Ancient Tree Groups and Meteorological Station Locations: Records the coordinates of Lhatse Meteorological Station and four sampling sites in both degrees-minutes-second and decimal degree formats, with no measurement units applied.2.

Meteorological Data of Lhatse Meteorological Station (1977–2020): Column headers include *Year*, *T* (mean annual temperature, unit: ℃) and *P* (annual precipitation, unit: mm), containing continuous annual meteorological records.3. Age Distribution of 64 Ancient Trees: Columns are *Age class/a* (unit: year) and *Number of trees* (unit: individual), presenting the age structure of all sampled ancient trees.4. Standard Tree-Ring Chronology of Ancient Tree Groups in Ngamring County, Western Xizang: Columns include *Year*, *Tree-ring index* and *Sample size*, recording annual tree-ring indices and corresponding sample quantities from 1855 to 2024.5.

Correlation between Tree-Ring Indices and Monthly Climatic Factors (1977–2020): Presents correlation coefficients of tree rings against monthly temperature and precipitation.6. Distribution Density of Individual Tree-Ring Widths: Columns correspond to unique IDs of 64 sampled trees, with each row representing density values of different tree-ring widths.7. Resilience and Resistance of Three Groups of Ancient Trees to Extreme Events: Records two core indicators — tree resistance and tree resilience, reflecting growth responses of different tree groups to extreme climatic events.8.

Tree Growth Asynchrony (TGA) of Ancient Tree Groups (1855–2024): Contains *Year* and TGA values, showing long-term variations in growth asynchrony of the ancient cypress populations.This .xlsx file is compatible with mainstream office software such as Microsoft Excel and WPS Office for viewing, editing and secondary data analysis, and no dedicated professional software is required. With standardized naming and clear data structures, it can be applied to academic research, dataset archiving, model construction and other scenarios.