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The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra‐annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra‐annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra‐annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid‐elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra‐annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios. [ABSTRACT FROM AUTHOR]

The Himalayan region has already witnessed profound climate changes detectable in the cryosphere and the hydrological cycle, already resulting in drastic socio-economic impacts. We developed a 619-yea-long tree-ring-width chronology from the central Nepal Himalaya, spanning the period 1399–2017 CE. However, due to low replication of the early part of the chronology, only the section after 1600 CE was used for climate reconstruction. Proxy climate relationships indicate that temperature conditions during spring (March–May) are the main forcing factor for tree growth of Tsuga dumosa at the study site. We developed a robust climate reconstruction model and reconstructed spring temperatures for the period 1600–2017 CE. Our reconstruction showed cooler conditions during 1658–1681 CE, 1705–1722 CE, 1753–1773 CE, 1796–1874 CE, 1900–1936 CE, and 1973 CE. Periods with comparably warmer conditions occurred in 1600–1625 CE, 1633–1657 CE, 1682–1704 CE, 1740–1752 CE, 1779–1795 CE, 1936–1945 CE, 1956–1972 CE, and at the beginning of the 21st century. Tropical volcanic eruptions showed only a sporadic impact on the reconstructed temperature. Also, no consistent temperature trend was evident since 1600 CE. Our temperature reconstruction showed positive teleconnections with March–May averaged gridded temperature data for far west Nepal and adjacent areas in Northwest India and on the Southwest Tibetan plateau. We found spectral periodicities of 2.75–4 and 40–65 years frequencies in our temperature reconstruction, indicating that past climate variability in central Nepal might have been influenced by large-scale climate modes, like the Atlantic Multi-decadal Oscillation, the North Atlantic Oscillation, and the El Niño-Southern Oscillation. [ABSTRACT FROM AUTHOR]

The Himalayas experienced long-term climate changes and recent extreme weather events that affected plant growth and the physiology of tree species at high-elevation sites. This study presents the first statistically robust δ 18 O TR chronologies for two native pine species, Pinus roxburghii, and Pinus wallichiana, in the lower Nepalese Himalaya. The isotope chronologies exhibited 0.88‰ differences in overall mean isotope values attributed to varying elevations (460-2000 m asl). Comparative analysis of climate response using data sets from different sources and resolutions revealed the superiority of the APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data set calibrated for the South Asian Summer Monsoon (SASM)-dominated region. Both species exhibited negative correlations with monsoon precipitation and positive correlations with temperature. However, during the peak monsoon season (July-August), daily resolved climate data disentangled statistically insignificant relationships, and revealed that δ 18 O TR is influenced by atmospheric moisture. Both congeneric species showed a decoupling between the chronologies after 1995. However, no significant change in air moisture origin and monsoon regime between the study sites was observed, indicating a consistent dominant moisture source during different monsoon seasons. Besides, we also observed the decreased inter-series correlation of both δ 18 O TR chronologies after 1995, with P. wallichiana experiencing a steeper decrease than P. roxburghii. The weakening correlations between and within the chronologies coincided with a regional drought during 1993-1995 in both sites, highlighting the strong regulation of local climate on the impact of regional extreme climate events. Our findings emphasise the importance of employing climate data with optimal spatial and temporal resolution for improved δ 18 O TR -climate relationships at the intra-annual scale while considering the influence of site-specific local environmental conditions. Assessing climate data sets with station data is vital for accurately interpreting climate change's impact on forest response and long-term climate reconstructions., (© 2024. The Author(s).)