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Article Abstract

The April-September maximum temperature in the Greater Caucasus region of Georgia has undergone notable changes, yet extended reconstructions remain scarce. We collected 40 Pinus sylvestris cores from Bakuriani and extracted their blue-intensity (BI) signals, which capture latewood density closely linked to high-season temperature. After chemical treatment and high-resolution scanning, we employed correlation analyses to identify the seasonal temperature signal in BI. Then, we used a linear regression model-validated by local instrumental records from 1950-2020-to reconstruct April-September temperatures back to 1780 CE. Additional superposed epoch analysis tested the reconstruction's responsiveness to significant volcanic eruptions and solar variability. Our reconstruction strongly correlates with observed data (r = 0.72, p < 0.001), revealing significant warming trends alongside cooling events linked to volcanic aerosols and low solar activity in recent decades. Spatial analyses confirm that the BI-derived temperature variations align well with broader regional climate patterns. Furthermore, CMIP6-based projections under high-emission scenarios suggest possible warming of up to 8.75°C by 2100, highlighting the severity of future climate risks in the region. By integrating BI data, linear regression techniques, and superposed epoch analysis, this research demonstrates the effectiveness of tree-ring proxies in capturing both anthropogenic and natural drivers of climate variability. The resulting 240-year temperature record provides valuable insights into historical climate dynamics, refines model predictions, and underscores the importance of localised, high-resolution data for adaptation planning in the Greater Caucasus region.

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http://dx.doi.org/10.1007/s00484-025-02930-7DOI Listing

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The April-September maximum temperature in the Greater Caucasus region of Georgia has undergone notable changes, yet extended reconstructions remain scarce. We collected 40 Pinus sylvestris cores from Bakuriani and extracted their blue-intensity (BI) signals, which capture latewood density closely linked to high-season temperature. After chemical treatment and high-resolution scanning, we employed correlation analyses to identify the seasonal temperature signal in BI.

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