The study area is located in the East European Plain and is characterized by cold winters, warm summers, and pronounced seasonality. The relatively flat topography of the East European Plain, together with the low relief of Central and Western Europe, allows Atlantic moisture carried by the prevailing westerlies to penetrate far inland toward the Ural Mountains (Klimenko and Solomina, 2010).

The geographic locations of the three sampling sites are shown in Fig. 1a and Table 1. The northern sampling site is located on the Solovetsky Islands, which have a subarctic climate. Owing to the maritime influence of the White Sea, the islands experience milder temperatures, smaller diurnal temperature ranges, higher humidity, and stronger winds than the adjacent mainland coast (Semenyak and Dolgova, 2023). The central sampling site is located in Yaroslavl city and has a temperate continental climate. The surrounding forests are mainly coniferous and dominated by Scots pine. The site lies within a forest park in an urban zone and may be influenced by anthropogenic disturbance. The southern sampling site is located in Khrenovskoy Forest (Voronezh Oblast) and has a temperate continental climate. The forest lies in the forest-steppe zone and represents the southernmost natural pine forest in the East European Plain (Matskovsky et al., 2017), with Scots pine as the dominant tree species (Matveev et al., 2017).

From 1950–2021 (precipitation for the central site is only available for 1967–2021; Fig. 1b–d), mean annual precipitation was 480, 623, and 489 mm at the northern, central, and southern sites, respectively. Mean annual temperatures were 1.52, 3.85, and 6.51 °C, respectively. Mean summer (June–August) temperature increased from 12.86 °C at the northern site to 16.73 °C at the central site and 19.66 °C at the southern site. The lowest mean RH occurred in June at the northern site (69.54 %) and in May at the central (63.56 %) and southern (56.71 %) sites (Fig. 1b–d).

Scots pine was selected as the study species. It is long-lived (up to ∼ 400 years) and widely distributed across northern Eurasia (Lange et al., 2018). Xylogenesis observations indicate pronounced latitudinal differences in growth phenology: in northern insular ecosystems near our northern site, growth typically begins in early June and ceases by mid to late August, whereas at more southerly sites xylem formation generally starts earlier (from late April to May) and ends later (from late August to September) (Matulewski et al., 2019; Matveev et al., 2020; Tishin et al., 2016).

Initially, the cores were collected for dendroclimatic analysis at breast height using increment corers from dominant and subdominant trees. The cores were mounted on wooden bases with polyvinyl alcohol (PVA) glue, polished, and scanned. Tree-ring widths (TRW) were measured in CooRecorder, and cross-dating was verified for all samples using COFECHA (Holmes, 1983). The TRW chronologies used in this study were developed by detrending and standardizing the ring-width series with the ARSTAN program. Previous analyses of TRW data from these sites are available in Dolgova et al. (2022) and Semenyak and Dolgova (2023).

The numbers of candidate samples and the results of core selection for δ18O_TRC analysis are summarized in Table S1 in the Supplement. The same selection criteria were applied at all three sites, including preferentially selecting the oldest cores to minimize potential juvenile effects, reliable cross-dating and continuous temporal coverage, clearly identifiable ring boundaries, and the ability to isolate individual rings without contamination from adjacent rings while obtaining sufficient α-cellulose for isotope measurement (Xu et al., 2011, 2013b). Narrow rings were retained as long as they could be reliably separated and provided sufficient material, whereas cores containing missing rings or rings that could not be reliably separated and measured were not selected. Based on these criteria, four cores from four different trees were initially selected from all candidate cores at each site. Additional sampling at the northern site in 2021 provided three further cores covering 2000–2021, allowing the isotope chronology to be extended to 2021. α-cellulose was extracted using a modified plate method (Xu et al., 2011, 2013b). Cores were cut into ∼ 1 mm thick, and ∼ 7 cm long slivers and processed following the Jayme–Wise protocol (Loader et al., 1997). Specifically, lignin, hemicellulose, and lipids were sequentially removed by treatment with acidified NaClO₂, 17 wt % NaOH, and organic solvents (acetone, toluene, and ethanol), respectively. After chemical treatment, the α-cellulose slivers were oven-dried at 70 °C. Annual α-cellulose subsamples were separated under a binocular microscope using a scalpel. The α-cellulose samples (0.12–0.20 mg) were weighed and wrapped in silver foil for oxygen isotope analysis.

δ18O_TRC values were measured at the Institute of Geology and Geophysics, Chinese Academy of Sciences, using an isotope ratio mass spectrometer (Delta V Advantage, Thermo Scientific, Germany) coupled to a high-temperature conversion elemental analyzer (Flash HT, Thermo Scientific, Germany). Oxygen isotope ratios are reported in δ notation relative to Vienna Standard Mean Ocean Water (VSMOW): δ18O = Rsample/Rstandard-1 × 1000 ‰, where Rsample and Rstandard denote the 18O/16O ratios of the sample and standard, respectively. For calibration and quality control, a Merck cellulose working standard was analyzed after every eight samples, and δ18O values were normalized to this standard. The analytical uncertainties for repeated measurements of Merck cellulose were ∼ ±0.16 ‰ (n = 149).

Temperature and precipitation data for the northern, central, and southern sites were obtained from the Kem'-Port Meteorological Station (34.80° E, 64.98° N; WMO No. 22520), Kostroma Meteorological Station (40.78° E, 57.73° N; WMO No. 27333), and Kamennaya Step' Meteorological Station (40.70° E, 51.05° N; WMO No. 34139), respectively (Fig. 1a). All meteorological station data were obtained from the Russian Institute of Hydrometeorological Information-World Data Centre (RIHMI-WDC).

To examine contemporaneous and lagged relationships between δ18O_TRC and additional climatic variables, we used gridded products from the following archives. These included monthly mean temperature (Tmean), maximum temperature (Tmax⁡), minimum temperature (Tmin⁡), precipitation (Pre), cloud cover (CC), and vapor pressure (VP) from CRU TS 4.08 at 0.5° × 0.5° spatial resolution for 1950–2021 (Harris et al., 2020); drought indices including the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) (Dai et al., 2004; Vicente-Serrano et al., 2010b); and soil moisture (SM; 0–10 and 0–40 cm) from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 0.1° × 0.1° resolution (McNally et al., 2017).

All gridded datasets were accessed and downloaded via the KNMI Climate Explorer (Trouet and Van Oldenborgh, 2013). For each site, gridded variables were spatially averaged within a 2° × 2° box centered on the sampling coordinates. Monthly Tmean and VP were used to calculate RH and VPD following formulae (Allen et al., 1998): 1RH=VP/esat×1002esat=6.108×exp⁡17.27×T/(T+237.3)3VPD=esat-VP where esat is saturated vapor pressure (hPa), and T is mean temperature (°C).

Given that δ18O_pis an important control on δ18O_TRC, and that nearby Global Network of Isotopes in Precipitation (GNIP; https://www.iaea.org/zh/fu-wu/wang-luo, last access: 5 November 2026) records are relatively short, GNIP observations alone are insufficient for long-term analyses. Therefore, we used the isotope-enabled atmospheric general circulation model ECHAM5-wiso to obtain δ18O_p for our study region. ECHAM5-wiso is an isotope-enabled extension of ECHAM5 in which the stable water isotopologues H218O and HDO are explicitly simulated within the atmospheric hydrological cycle (Werner et al., 2011). We downloaded model-simulated monthly δ18O_p (10.1594/PANGAEA.902347, Werner, 2019) and extracted time series from the grid cell nearest to each study site for 1958–2013. Previous studies have evaluated ECHAM5-wiso against GNIP observations at the global scale (Werner et al., 2011) and across Europe (Langebroek et al., 2011). Moreover, ECHAM5-wiso successfully reproduced the seasonal phase of δ18O_p observed at nearby GNIP stations, despite a pronounced station-dependent mean offset, particularly at southern stations with limited GNIP coverage (Fig. S1 in the Supplement). Consequently, we primarily used ECHAM5-wiso δ18O_p simulations to interpret interannual variability in source-water isotopes. The latitude, longitude, and other metadata for the simulated and observed δ18O_p data are provided in Table S2.

The δ18O_TRC series from all analyzed cores at each site were averaged to generate site-level δ18O_TRC chronologies for the northern, central, and southern sites. We calculated the mean interseries correlation (Rbar) to evaluate inter-core coherence and the expressed population signal (EPS) to assess chronology representativeness (Wigley et al., 1984). Rbar was calculated as the mean of all pairwise Pearson correlations among the four original core series at each site, and EPS was derived from Rbar using a fixed sample replication of N = 4. Because the isotope chronologies span only approximately 65–72 years, Rbar and EPS were calculated over the common period of the four original core series at each site rather than using moving-window analyses. The three additional northern series cover only approximately 2000–2021 and were therefore excluded from these calculations.

To explore the factors controlling the spatial differences in δ18O_TRC, we used a simplified mechanistic fractionation framework for δ18O_TRC (Roden et al.,  2000):

4δ18OTRC=δ18Os+ϵ0+α1-f01-hϵ*+ϵk Here, δ18O_s is the oxygen isotopic composition of source water (approximated by δ18O_p); ϵ0 is the net biochemical fractionation term associated with cellulose formation; f0 is the proportion of exchangeable oxygen during cellulose synthesis (set to 0.42; Sternberg and Ellsworth, 2011); ϵ* and ϵk are the equilibrium and kinetic fractionation factors, respectively (set to 9 ‰ and 29 ‰, respectively; Allison et al., 1985); h is the relative humidity at the evaporative site in the leaf; and α is a scaling/attenuation factor describing the transfer of the evaporative-enrichment signal to cellulose (set to 1; Allison et al., 1985; Barbour, 2007).

We further defined the evaporative enrichment term, E, as 5E=α1-f01-hϵ*+ϵk Here, Δ denotes the pairwise difference between sites. We then calculated Δδ18O_TRC, Δδ18O_p, ΔRH, and ΔE to quantify the relative contributions of site-to-site differences in δ18O_p and RH-controlled evaporative enrichment to spatial variability in δ18O_TRC.

To identify climatic and source-water signals recorded in δ18O_TRC, we correlated the chronology with monthly and seasonal climate variables from June of the previous year to September of the current year, and with δ18O_p from October of the previous year to September of the current year. Spatial correlations between each site chronology and gridded CRU TS 4.08 data were calculated using the KNMI Climate Explorer (https://climexp.knmi.nl/start.cgi, last access: 5 November 2026) to evaluate the regional representativeness of δ18O_TRC.

The mean inter-series correlation (Rbar) for the northern δ18O_TRC site was 0.67, and the expressed population signal (EPS) of the chronology was 0.89 (Table 1). By contrast, the central and southern sites showed slightly lower values (Rbar = 0.57 and 0.58, respectively; EPS = 0.84 for both sites; Table 1). Along the north–south transect, the series were averaged to build δ18O_TRC chronologies for the North (1950–2021), Central (1950–2020), and South (1950–2014) (Fig. 2a–d). The original four-tree and expanded seven-tree chronologies at the northern site showed similar interannual variability over their overlapping period (Fig. S2a). EPS was ≥ 0.84 at all three sites, suggesting overall coherent inter-tree signals within each site. The high within-site inter-series correlations (Table S3) further support the reliability of the δ18O_TRC chronologies.

The northern δ18O_TRC chronology ranged from 24.85 ‰ to 27.99 ‰, with a mean of 26.36 ‰, a standard deviation (SD) of 0.60 ‰, and a first-order autocorrelation (AC1) of -0.09 (Table 1). The central chronology ranged from 26.23 ‰ to 28.76 ‰, with a mean of 27.61 ‰, an SD of 0.61 ‰, and an AC1 of 0.15 (Table 1). The southern chronology ranged from 28.93 ‰ to 31.79 ‰, with a mean of 30.52 ‰, an SD of 0.68 ‰, and an AC1 of 0.13 (Table 1). Over the common period (1950–2014), the northernmost Solovetsky Islands chronology had the lowest median (26.35 ‰), the central site was 27.62 ‰, and the southernmost site had the highest median (30.54 ‰) (Fig. 2e). Overall, both the mean and median values of the East European Plain δ18O_TRC chronologies show a clear latitudinal gradient, with higher values in the south and lower values in the north.

Pairwise correlations among the three chronologies ranged from 0.14 to 0.35 over 1950–2014. Significant correlations were found for the North–Central pair (NC; r = 0.33, p < 0.01) and the Central–South pair (CS; r = 0.35, p < 0.01), whereas the North–South pair (NS; r = 0.14, p > 0.05) was not significant (Fig. 2e). Overall, these results suggest that δ18O_TRC variability shows local coherence, with stronger similarity between adjacent sites that weakens with increasing distance.

During the growing season or summer, δ18O_TRC is significantly positively correlated with VPD, but significantly negatively correlated with moisture-related variables such as Pre, RH, CC, SPEI, PDSI, and SM (Fig. 3; Tables S4–S6). Among the three sites, the northern site shows the strongest atmospheric drying signal, with the highest correlation between δ18O_TRC and May–September VPD (r = 0.61, p < 0.01, n = 72) (Fig. 3; Table S4). In contrast, moisture signals are weaker at the central and southern sites: δ18O_TRC at central site correlates with May–September SPEI at r = -0.42 (p < 0.01, n = 69), whereas the strongest relationship at the southern site is with June–August SPEI (r = -0.51, p < 0.01, n = 65) (Fig. 3; Tables S5–S6). By comparison, moisture conditions in the previous year and in spring (March–May) exert relatively limited influence on δ18O_TRC, with only a few variables showing significant effects in isolated months.

Overall, correlation analyses between δ18O_TRC and local climate factors indicate that all three sites consistently capture hydroclimatic variability. Results based on first-differenced chronologies further support these patterns (Fig. S3). Correlation strengths at the northern site changed little after differencing, whereas correlations with moisture-related indices become stronger at the central and southern sites, indicating that the interannual correspondence between δ18O_TRC and hydroclimate becomes clearer once low-frequency variability is reduced (Fig. S3). Notably, at the northern site, the original four-tree and expanded seven-tree chronologies also showed broadly consistent climate-response patterns, particularly for summer hydroclimatic variables (Fig. S2b, c).

In addition to the moisture signal, δ18O_TRC at all three sites also exhibits significant temperature responses. The central site shows the strongest summer temperature signal, with δ18O_TRC correlated with June–September Tmax⁡ (r = 0.45, p < 0.01, n = 71) (Fig. 3), and this relationship remains nearly unchanged when using first-differenced series (Fig. S3). Notably, the temperature influence is not confined to summer, but extends into the previous cold season and the current spring. Further analyses show stronger correlations between δ18O_TRC and broader seasonal temperature windows: the northern site correlates with March–September Tmax⁡ (r = 0.50), while the central and southern sites correlate with mean temperature from the previous October to the current September (both r = 0.48), exceeding the strength of summer-only temperature signals.

Source-water signals in the three δ18O_TRC chronologies are shown in Fig. 4. At all sites, δ18O_TRC showed its strongest correlations with δ18O_p in early summer (June–July), with r = 0.47, 0.68, and 0.42 from north to south (p < 0.01; Fig. 4). Following first-order differencing, these correlations changed only slightly (Fig. S4). At the northern site, δ18O_TRC was also positively correlated with δ18O_p in January and March of the current year. At the central site, δ18O_p in the previous September, October, and December, as well as in January and May of the current year, was positively associated with δ18O_TRC. In contrast, at the southern site, δ18O_p outside June–July showed no significant relationship with δ18O_TRC.

For the spatial difference between the northern and central sites (NC), the δ18O_TRC difference is -1.288 ‰. In this case, Δδ18O_p (-1.159 ‰) is larger than the difference in the enrichment term (-0.688 ‰), indicating that source-water isotopic differences dominate the NC δ18O_TRC spatial offset (Table 2). In contrast, the δ18O_TRC difference between the central and southern sites (CS) is -2.884 ‰, and the enrichment-term difference (-2.242 ‰) exceeds Δδ18O_p (-1.295 ‰). Together with the larger RH difference (ΔRH = 10.174 %), this suggests that stronger evaporative enrichment is the primary driver of the δ18O_TRC offset between the Central and Southern sites (Table 2). The overall difference between the δ18O_TRC from northern and southern sites (NS) is -4.172 ‰, with comparable magnitudes for Δδ18O_p (-2.454 ‰) and the enrichment term (-2.930 ‰) (Table 2). Estimates based on observed δ18O_p also support this pattern, showing a consistent north–south gradient (Table S7).

Spatial correlation analyses based on gridded hydroclimate metrics (SPEI, VPD, and RH) indicate that δ18O_TRC at all three sites exhibited a consistent response to regional summer moisture conditions: it shows significant positive correlations with June–August VPD over broad areas, but significant negative correlations with RH and SPEI (Fig. 5). At the northern site, the strongest correlations formed a spatially coherent significant region over the White Sea area and adjacent northern Europe (Fig. 5a, d, g). The central site showed a similar but weaker pattern, with significant regions mainly covering the central East European Plain and extending eastward (Fig. 5b, e, h). At the southern site, significant regions were primarily distributed over the southern East European Plain (Fig. 5c, f, i). In addition, spatial correlations with ECHAM5-wiso–simulated δ18O_p further suggested a coherent source-water isotopic imprint in δ18O_TRC (Fig. S5). Overall, δ18O_TRC reflected both regional summer hydroclimate and δ18O_p variability, with broadly consistent spatial patterns across sites but differing strengths.

The climatic sensitivity of TRW differs markedly among the three sites (Fig. 6). Because the SM record is relatively short, we do not use it as the primary basis for identifying dominant controls, although its correlations are significant. TRW at the northern and southern sites shows significant summer hydroclimate sensitivity, with the strongest relationships in both cases occurring for PDSI (north: July, r = 0.36; south: June–September, r = 0.54; Fig. 6a, c). By contrast, TRW at the central site shows little evidence of a stable moisture response (Fig. 6b). TRW at all three sites is also significantly sensitive to temperature, but the optimal response window shifts earlier toward lower latitudes: the northern site responds mainly to midsummer (July–August) temperature, the central site to early spring (March–April), and the southern site to late winter–early spring (January–March) (Fig. 6). Overall, the climatic responses of TRW exhibit clear regional differentiation: temperature and moisture signals are of comparable strength at the northern site, the central site is dominated by temperature, and the southern site is primarily controlled by moisture conditions.

δ18O_p and RH-regulated leaf-water evaporative enrichment jointly control spatial variations in δ18O_TRC (Roden et al., 2000). Across the East European Plain, δ18O_p shows higher values in the south than in the northern region (Fig. S6), whereas regional RH exhibits a wetter north–drier south gradient (Fig. 1). These large-scale gradients are broadly consistent with the southward increase in δ18O_TRC across the region (Table 1; Fig. 2). Further analyses indicate that the dominant controlling factors on the spatial variability of δ18O_TRC are different between the NC and CS site pairs.

For the NC pair, the between-site difference in δ18O_p is clearly larger than the corresponding difference in RH-driven evaporative enrichment (Table 2), indicating that δ18O_p is the primary driver. This pattern is expected because δ18O_TRC partly reflects the isotopic composition of source water via soil-water uptake and therefore often covaries positively with δ18O_p (Saurer et al., 2000). In mid- to high-latitude regions, the “amount effect” is generally weak, whereas temperature often exerts a dominant control on δ18O_p (Krklec et al., 2018; Rozanski et al., 1992). At higher latitudes, lower condensation temperatures and stronger isotopic fractionation, together with progressive rainout during poleward transport, deplete ¹⁸O in the remaining vapor and result in lower δ18O_p values (Dansgaard, 1953; Gat, 1996). Global observations support this mechanism, showing an average decrease of ∼ 0.5 ‰ per 1° increase in latitude (Rozanski et al., 1992). In addition, the RH difference between the northern and central sites is relatively small (Figs. 1; S6), resulting in a relatively limited between-site contrast in evaporative enrichment. Together, these mechanisms explain why source-water isotopes dominate spatial difference in δ18O_TRC between the NC sites.

For the CS pair, the RH contrast is much larger than that in the NC pair (Figs. 1; S6). Consistently, the between-site difference in RH-driven evaporative enrichment exceeds the corresponding difference in δ18O_p (Table 2), indicating that drier southern conditions enhance evaporative enrichment and contribute more to Δδ18O_TRC. Lower RH strengthens the leaf-to-atmosphere evaporative gradient, increasing ¹⁸O enrichment in leaf water and its transfer to cellulose (Roden et al., 2000). A modeling study further indicates that spatial variation in leaf-water evaporative enrichment is closely linked to atmospheric humidity, with drier conditions favoring greater ¹⁸O enrichment (Keel et al., 2016). Similar humidity-driven patterns have been reported elsewhere, for example along a north–south gradient in the tropical Andes, where δ18O_TRC increases toward drier southern sites (Álvarez et al., 2024).

Comparable large-scale latitudinal patterns in δ18O_TRC has also been documented in other regions, including the European tree-ring δ18O network (Balting et al., 2021), the Asian tree-ring δ18O network (Huang et al., 2025; Xu et al., 2021, 2024), and eastern Anatolia (Mutlu et al., 2012). Across Europe, spatial variability in tree-ring δ18O has been attributed to the combined effects of latitude, elevation, and continentality (Balting et al., 2021; Mutlu et al., 2012). Across Asia, the tripolar pattern in tree-ring δ18O primarily reflects the latitudinal structure of δ18O_p, with RH-controlled enrichment acting as a key modulator, particularly in mid-latitudes (Huang et al., 2025; Xu et al., 2024). Similar to patterns reported in Asian, spatial variability in the East European Plain reflects a dual control: the mid- to high-latitude NC pair is dominated by δ18O_p, whereas the mid-latitude CS pair is driven primarily by RH-controlled enrichment. Together, these mechanisms produce a south-to-north decrease in δ18O_TRC across the region.

Based on the mechanistic framework of cellulose δ18O, δ18O_TRC integrates both δ18O_p and evaporative enrichment of leaf water (McCarroll and Loader, 2004; Roden et al., 2000). Across the three sites, δ18O_TRC records a coherent interannual signal during the growing season and summer: values tend to increase under higher evaporative demand and more enriched δ18O_p, and decrease under more humid conditions (Figs. 3; S3).

On the source-water side, interannual variability in source-water δ18O is linked to temperature- and precipitation-related processes (Dansgaard, 1964; Rozanski et al., 1992). Higher temperature and VPD can enhance soil evaporation and enrich near-surface soil water, whereas higher precipitation and SM tend to reduce evaporative enrichment and δ18O_s. In parallel, the strong linkages of δ18O_TRC with moisture indicate leaf-water evaporative enrichment as a key control (McCarroll and Loader, 2004; Roden et al., 2000), because root uptake involves negligible isotopic fractionation (Dawson and Ehleringer, 1991). Lower RH (higher VPD) enhances leaf-water ¹⁸O enrichment and its transfer to cellulose (McCarroll and Loader, 2004; Roden et al., 2000); CC likely reflects coupled radiative–humidity effects on enrichment (Wang et al., 2022). Finally, SPEI and PDSI integrate precipitation supply and evaporative demand (Dai et al., 2004; Vicente-Serrano et al., 2010b), which is consistent with their coherent relationships with δ18O_TRC as integrated indicators of hydroclimate (Xu et al., 2013a).

Despite the overall similarity in response patterns, the dominant controls on interannual δ18O_TRC variability differ among sites. The central site exhibits both a pronounced source-water isotopic imprint and a leaf-water enrichment signal (Figs. 3–4; S3). Significant correlations between δ18O_TRC and δ18O_p in both the original and first-differenced series indicate that interannual anomalies in source-water δ18O are effectively transmitted into cellulose. Together with the relatively larger interannual variability in δ18O_p and smaller variability in RH (Fig. S6), this suggests that the source-water signal is more readily expressed statistically. By contrast, correlations with humidity-related metrics become markedly stronger after differencing, indicating that low-frequency variability partly obscures the interannual hydroclimate signal (Fig. S3). The weaker humidity signal in the original series may reflect the urban forest-park setting of the central site, where canopy buffering and local anthropogenic influences can decouple near-canopy humidity from the broader gridded macroclimatic background at low frequencies (De Frenne et al., 2019; Zellweger et al., 2020). In addition, the strong Tmax⁡ response changes little after differencing, implying that it is not solely trend-driven and may partly reflect temperature-related modulation of regional δ18O_p and associated processes (Field et al., 2022; Leland et al., 2023).

In contrast, humidity signals are more pronounced at the northern and southern sites, although the underlying mechanisms differ. The northern δ18O_TRC chronology shows a stable VPD signal (Figs. 3; S3; S7). The Solovetsky Islands lie in a cold and moist high-latitude environment (Fig. 1), where tree growth is more strongly temperature-limited. Under such conditions, warming tends to increase atmospheric evaporative demand (Zharkov et al., 2021), allowing evaporative-demand–driven leaf-water enrichment to exert a stable control on interannual δ18O_TRC variability. Similarly, studies from high-latitude northern Europe have reported clear summer VPD signals in δ18O_TRC and have used δ18O_TRC networks to reconstruct regional-scale VPD (Treydte et al., 2024). By comparison, the southern site is characterized by a drier climatic background with lower RH (Figs. 1; S6), and δ18O_TRC shows its strongest relationship with SPEI, consistent with evaporative enrichment being jointly constrained by moisture supply and atmospheric demand. Numerous SPEI reconstructions based on δ18O_TRC have also been reported for mid-latitude Europe (An et al., 2023; Freund et al., 2023; Labuhn et al., 2016).

Together, these results highlight the potential of δ18O_TRC for hydroclimate reconstructions in the East European Plain. The northern δ18O_TRC chronology exhibits a strong and stable growing-season VPD signal (Figs. 3; S3; S7), indicating particularly strong potential for high-latitude hydroclimate reconstruction. Meanwhile, the significant coherence among chronologies from nearby sites (Fig. 2e), together with the common sensitivity of the three chronologies to regional summer hydroclimate (Fig. 5), underscores the potential of δ18O_TRC for reconstructing regional-scale hydroclimate variability and supports expanding site coverage to improve the spatial representativeness of future reconstructions. However, the low replication and EPS values of 0.84 at the central and southern sites introduce uncertainty in chronology representativeness and should be considered when evaluating their regional applicability and reconstruction potential.

Moisture signals in TRW at the northern and central sites are markedly weaker than those in δ18O_TRC (Figs. 3; 6). This contrast is consistent with the growth limitation commonly observed at mid- to high latitudes, where TRW north of approximately 50–60° N is often primarily constrained by summer temperature and is therefore better suited for temperature reconstructions (Dolgova et al., 2022; Solomina and Matskovsky, 2021). By contrast, δ18O_TRC reflects both source-water isotope composition and leaf-water evaporative enrichment, allowing it to retain sensitivity to atmospheric humidity and evaporative demand even when radial growth is not strongly moisture-limited (McCarroll and Loader, 2004; Roden et al., 2000). Thus, in relatively humid, temperature-limited mid- to high-latitude settings, δ18O_TRC may outperform TRW in characterizing hydroclimate variability.

At the drier southern site, the strongest correlation between TRW and PDSI is comparable in magnitude to the strongest correlation between δ18O_TRC and SPEI (Figs. 3; 6). In addition, relative to other humidity metrics, δ18O_TRC shows weaker associations with PDSI in both the original and first-differenced series, whereas TRW mainly reflects PDSI and SM signals (Fig. 6). Because PDSI is widely used to represent drought severity and persistence and is closely related to soil-moisture conditions (Dai et al., 2004), this suggests that under stronger moisture limitation, TRW more directly tracks soil-moisture constraints on radial growth. By contrast, SPEI integrates precipitation supply and potential evapotranspiration and therefore better represents moisture conditions shaped jointly by water supply and atmospheric demand (Vicente-Serrano et al., 2010a). Accordingly, at the drier southern site, TRW tends to preferentially capture soil-moisture variability, whereas δ18O_TRC is more likely to integrate both source-water signals and evaporative enrichment processes.

The contrasting climate responses of δ18O_TRC and TRW observed in this study indicate that different tree-ring proxies record complementary climatic information. Previous studies have shown that, in temperature-limited environments, maximum latewood density (MXD) and some quantitative wood-anatomical traits generally exhibit stronger warm-season temperature signals than TRW and have been widely used to reconstruct warm-season temperature variability (Björklund et al., 2019, 2023). By contrast, stable carbon and oxygen isotopes can provide complementary information on tree physiological responses and hydroclimatic processes (McCarroll and Loader, 2004). Therefore, future multiproxy studies combining TRW, MXD, quantitative wood anatomy, and stable carbon and oxygen isotopes across the East European Plain could help more effectively disentangle regional temperature and moisture signals.