Response of Pinus sylvestris var . mongolica to water change and the reconstruction of drought history for the past 260 years in northeast China

We present a 260-year annual PDSI reconstruction based on a regional tree-ring width chronology of Scots 6 pine (Pinus sylvestris var. mongolica) from four sample sites in the Daxing’an Mountains, northeast China. The model 7 explained 38.2 % of the variance of annual PDSI during the calibration period from 1911 to 2010. Compared with local 8 historical documents, nearby forest fire history data and hydroclimate reconstructions, our reconstruction is accurate 9 and representative, and recorded the same dry years/periods. The drought of 1920s-1930s was more severe in the 10 Daxing’an Mountains than in surrounding areas. A moisture increase caused by a recent rapid warming (warm-wet 11 pattern) was identified for the Daxing’an Mountains, while a warm-dry pattern was found for the West-Central 12 Mongolian Plateaus (mild drier) and their transition zones: the East Mongolian Plateaus (severe drier). Overall, the 13 dry/wet variability of the Daxing’an Mountains and its relationship with the surrounding areas might be driven by 14 Pacific and Atlantic Ocean oscillations (e.g., ENSO, PDO, AMO, NAO and SNAO) that influence the Asian monsoon, 15 and in turn the local temperature and precipitation that influences regional drought. However, the Monsoon Asia 16 Drought Atlas of “Cook” might inaccurately portray dry/wet variations in the Daxing’an Mountains. 17


Introduction
Drought as an important climate driver that is occurring more frequently with climate change and is a focus of scientific efforts around the world (Bao et al. 2015;Cook et al. 2010;Dai 2011Dai , 2013;;Davi et al. 2006;Li et al. 2016).Severe droughts can threaten agriculture and social activities, and also has a devastating impact on human lives and the survival of native and domestic plants and animals (Bao et al. 2015;Cook et al. 2010;Dong et al. 2013;Shen 2008;Sun 2007).
Drought is one of the most severe and frequent natural disasters in China, especially in semi-arid and arid regions (Bao et al. 2015;Chen et al. 2015;Cook et al. 2010;Dong et al. 2013;Liang et al. 2006;Shen 2008;Sun and Liu 2013; Xu  1998).For instance, the 1920s severe drought affected all of northern China with significant economic losses to society and the economy (Dong et al. 2013;Liang et al. 2006;Shen 2008;Sun 2007).Natural droughts are preserved in the rings of trees in arid or semi-arid regions (Bao et al. 2015;Chen et al. 2015;Liang et al. 2006;Sun and Liu 2013;Wang and Song 2011).Recent studies indicate a trend of increasing drought frequency, persistence and severity due to global warming in many regions of the world (Bao et al. 2015;Cook et al. 2010;Dai 2011Dai , 2013;;Schrier et al. 2013).A rapid and pronounced warming accompanied by a decrease in precipitation has occurred in China, especially in high latitude and high altitude regions (Bao et al. 2015;Chen et al. 2015;Cook et al. 2010;Dai 2013;Sun and Liu 2013;Zhu et al. 2017), producing severe and prolonged drought in recent decades, for example from 1999 to 2002 (Bao et al. 2015;Liu et al. 2009;Shen 2008).
The Daxing'an Mountains in northeast China is the transition zone from semiarid climate in the east to more arid conditions in the west, and monsoon driven precipitation in the south to a non-monsoon climate in the north (Bao et al. 2015;Zhao et al. 2002).The Asian monsoon system has a direct impact on the occurrence, intensity and severity of droughts and floods (Bao et al. 2015;Cook et al. 2010;Liang et al. 2006;Wang et al. 2013;Wang et al. 2005;Zhao et al. 2002) that can have devastating effects on human society and economy as well as natural ecosystems (Sun 2007;Xu 1998).For instance, the drought of 2009 affected all of northeast China, with limited irrigation water available to more than 720,000 hectares of farmland, or drinking water for 81 million people (http://www.chinadaily.com.cn/cndy/2009-08/13/content_8562996.htm).In addition, drought affected regions had a higher risk of forest fires in spring and summer (Sun 2007).Drought can facilitate the occurrence of large wildfires.
For example, the Heilongjiang River fire in May 1987 killed over 200 people and burned ~73,000 km 2 (Sun 2007;Yao et al. 2017).
To better characterize current and future drought it is important to understand past drought patterns and their potential forcing mechanisms.However, the required meteorological records for the Daxing'an Mountains in northeast China only began in the 1950s.Therefore, tree rings can provide an important high resolution proxy for long-term drought reconstructions (Cook et al. 2010;Dai 2011;Pederson et al. 2013), although few studies have been conducted in north China, especially for hydroclimate reconstructions (Bao et al. 2015;Lv and Wang 2014;Wang and Lv 2012).Cook et al. (2010) reconstructed June-July-August Dai-PDSI for 534 grid points (Monsoon Asia Drought Atlas, MADA) in monsoon influenced Asia using a chronology developed from 327 tree-ring series.However, recent studies show that there has been some divergence of tree-ring-based drought reconstruction between the MADA and the individual sampling site or instrumental drought data, which might be caused by an insufficient spatiotemporal distribution of the tree-ring network used by MADA, especially in eastern Asia (Li et al. 2015;Liu et al. 2016) researchers have used the Palmer drought severity index (PDSI), calculated from a water balance equation, incorporating air temperature and precipitation, to estimate drought periodicity and intensity (Bao et al. 2015;Cook et al. 2010;Dai 2011;Sun and Liu 2013).Here, we present a 260-year reconstruction of annual PDSI using tree ring chronologies from the Daxing'an Mountains to identify the timing of droughts and their correlation with eastern Mongolian Plateaus climate as well as their potential forcing mechanisms.

Study area and climate
The Daxing'an Mountains, in northeast Inner Mongolia and north-western Heilongjiang Province, form an important natural geographic divide between the Pacific Ocean and the north-western arid inland (Fig. 1).It is known to be a transition zone between regions with semiarid and arid, and monsoon and non-monsoon driven climates (Zhao et al. 2002).The summer monsoons from the south-east are blocked by these mountains and cannot penetrating farther to the northwest.The western region is most arid, while farther east the climate is more humid and the slopes are forested.
Summer weather is characterized by periodic incursions of warm, humid air masses from low-latitude oceans.During the winter, dry and cold air persists air masses invade from high latitudes.
This study was conducted in the high-latitude forested portion of the Daxing'an Mountains.The forests are dominated by Dahurian larch (Larix gmelinii Rupr.) and Scots pine (Pinus sylvestris L. var.mongolica Litv.).Soils are predominantly brown coniferous and dark-brown forest peat (Xu 1998).Meteorological data from stations nearest to our sample sites (Xiaoergou station; Table 1) have an annual mean temperature range from -2.6 to 2.0 °C.The extreme coldest and hottest months are January (-39.5 °C) and June (32.8°C).Annual precipitation ranges from 289 to 1000 mm (averaging 500 mm) with high interannual variations.Rain during June to August accounts for 68% of total annual precipitation (Fig. 2).Low relative humidity occurs in all months outside of the growing season.Severe drought occurs frequently, especially in spring and summer (Sun 2007), and produces high fire risk.This region has the highest average annual burned area in China (Sun 2007).

Tree-ring data
Trees were sampled from four little-disturbed Mongolian pine-dominated sites in centre Daxing'an Mountains in May 2011 and 2012 (Table 1; Fig. 1).The sites are separated by more than 100 km (Figure 1).One core was obtained at breast height from 120 living old trees using a 5.15-mm-diameter increment borer (500 mm length, two screws, Haglöf Sweden, Lä ngsele, Sweden) (see Table 2 for detailed information).Each sample tree was selected to avoid the influence of identifiable stand disturbances (including animal and human disturbance, windstorm, snow and fire damage) and any obvious abnormal growth.All cores were dried, mounted, surfaced, and cross-dated following standard techniques of dendrochronology (Cook and Kairiukstis 1990;Fritts 1976).Ring widths were measured with a precision of 0.001 mm using a Velmex measuring system (Velmex, Inc., Bloomfield, NY, USA).
The quality of cross-dating and measurement was evaluated using the COFECHA program (Holmes 1983).Two cores, that were weakly correlated with the master chronology were excluded from further analysis.Successively, the agerelated trends were removed by fitting a cubic smoothing spline with a 50% frequency response cut-off at 2/3 of the series length using the ARSTAN program (Cook and Kairiukstis 1990).Tree-ring indices were calculated as ratios from the estimated growth curves.Autocorrelation was removed by autoregressive modelling, and site chronologies were calculated using a bi-weighted robust mean (Cook and Kairiukstis 1990).Standard dendrochronological statistics were computed to evaluate the quality of chronologies between tree mean correlation (Rbt) (Cook and Kairiukstis 1990) and ring mean sensitivity (Fritts 1976) (Table 3).The four chronologies have high values in standard deviation, mean sensitivity, mean series correlation and agreement within population.The chronologies reflect high inter-annual variation and a strong common signal and are excellent proxies for regional climate.Since all four chronologies agree well (Table 3), we merged all samples to develop a single robust regional chronology.Running RBAR (mean correlation between series) and EPS (expressed population signal) statistics were calculated for 51-year intervals of the chronology with 25-year overlaps to assess confidence in the chronology.RBAR averages variance among ring width series in a chronology, which estimates chronology signal strength (Cook and Kairiukstis 1990).EPS estimates the degree to which the chronology represents a hypothetical chronology based on a finite number of trees that match a hypothetically perfect chronology; EPS values greater than 0.85 are generally considered to be an acceptable threshold for a reliable chronology (Wigley et al. 1984).The regional chronology spanned the period from 1725 to 2010, and the reliable interval (EPS > 0.85) was 1751-2010 corresponding to eight cores/trees.

Climate and statistical analyses
Climate data were obtained from the China Meteorological Data Sharing Servicing System.The closest weather station to the sample sites is Xiaoergou ( The gridded climate dataset is much longer and has high homogeneity and coherency with instrumental records (Fig.
2), the gridded monthly total precipitation (CRU GPCC; Schneider et al. (2015)) and mean temperature (CRU TS3.drought index, was used to assess the effects of drought.Correlation analyses between the regional chronology and monthly climatic records were calculated from the previous July to the current July. A linear regression model was used to develop the drought reconstruction, and a traditional split-period calibration verification method was applied to examine model fit (Fritts 1976).Statistical parameters included the R 2 , Sign test (ST), reduction of error (RE), coefficient of efficiency (CE), product means test (PMT) and root mean square error (RMSE) (Cook and Kairiukstis 1990;Fritts 1976).Spatial correlation of measured and reconstructed drought variables with regional gridded CRU-PDSI (Schrier et al. 2013) were performed to examine the spatial representativeness of our reconstruction using the KNMI climate explorer.We also carried out the superposed epoch analysis (SEA) between nearby forest fire history and drought variables to further validate the accuracy of our reconstruction, since seasonal or annual droughts are usually a key factor in forest fire severity in the Daxing'an Mountains (Shen 2008;Sun 2007).Two Daxing'an Mountains (Bao et al. 2015), and the tree-ring-based streamflow reconstruction of Selenge River (SR) from Davi et al. (2006) in the Mongolian Plateaus, Mongolia) were evaluated and described by filtering and moving correlations.To identify spatialtemporal patterns of drought in Northeast Asia and their relationship with our reconstructed drought history, we further analyzed correlations with four other hydroclimatic reconstructions from the Daxing'an Mountains and the Mongolian Plateaus (Fig. 1).To make the comparison better visualized, all above series were standardized using Z-scores and then smoothed with a 21-year moving averaged to highlight low-frequency drought signals.
To evaluate the extreme dry and wet years in the historical period, we defined extremely dry and wet years with the annual PDSI value being lower or higher than the average +/-1.5 SD.We assessed the multiyear dry/wet periods based on the intensity (average departure values from the long-term mean) and magnitude (cumulative departure values from the long-term mean).A spectral analysis were applied to identify the periodicity of dry/wet variability and possible effects of large-scale climate using Multi-taper method (MTM) program (Mann and Lees 1996).To further confirm the linkage between large-scale climate and regional drought, we analysed their relationship with Pearson correlation

Tree growth-climate relationships
The radial growth of Scots pine was significantly (p < 0.05) positively correlated with precipitation in all months except the previous November and current February (Fig. 4a).Temperature of the previous November to current May (except for current April) was significantly correlated with ring widths at the 95% confidence level (Fig. 4a).The highest positive Pearson's correlation coefficients were found between the ring width Scots pine chronology and monthly total precipitation of October (r = 0.35, p < 0.05) and previous December temperature (r = 0.35, p < 0.05).Radial growth of Scots pine in the Daxing'an Mountains was influenced by both precipitation and temperature, but the effects of precipitation were stronger, which revealed annual precipitation sensitivity of the Scots pine chronology during the last century (Fig. 4a).Furthermore, we calculated the correlation between the tree-ring index and Dai-PDSI (common period of 1901-2010), which takes into account temperature and precipitation (Dai 2011).Significant (p < 0.05) positive correlations between tree rings and PDSI was found for all months from the previous July to the current July (Fig. 4b).
The correlation between tree growth and annual (Jan-Dec) average PDSI had the highest correlation (r = 0.62, p < 0.0001, n = 110) between tree growth and PDSI data among the annual, seasonal or individual month scales.The results confirmed that water conditions had a significant controlling influence on Scots pine growth over a last century (Fig. 6).

PDSI reconstruction
The regression model between the tree-ring indices (predictors) and annual PDSI (predicted) for the calibration period was as follows: where Dt is the annual PDSI and It is the tree-ring index at year t.For the calibration period 1911-2010, the reconstruction explained 38.2% of the PDSI variation (37.6% after accounting for the loss of degrees of freedom).As shown in Figure 5a, the actual and estimated annual PDSI of Daxing'an Mountain have similar trends and are parallel to each other during the calibration period.However, the estimated PDSI did not capture the magnitude of extreme dry or wet conditions.Spatial correlation analysis show that the actual and estimated PDSI had a strong and similar spatial correlation pattern with the Northeast Asia grided scPDSI (0.5°×0.5°) (Fig. 6).The split calibration-verification test showed that the explained variances were high during the two calibration periods and the statistics of R, R 2 , ST, PMT are all significant at p < 0.05, which indicated that the model was reliable (Table 4).The most rigorous tests, RE and CE, were also positive for both verification periods (Cook and Kairiukstis 1990; Fritts 1976) (Table 4).

Drought-wet variations
The reconstructed annual PDSI with 11-year moving average exhibited a mean of 0.48 and a standard deviation (SD) of ±1.15 during the past 260 years (Fig. 5b).Reconstruction of the annual PDSI displayed strong interannual to decadal scale variability throughout the period 1751 to 2010.During the last 260 years, there were 22 extreme dry years (accounting for 8.5%) and 15 extreme wet years (5.8%) (Table 5).Most extreme dry years occurred in the 19 th (12 years, accounting for 48%) and 20 th (9 years, accounting for 36%) centuries, and a majority of extreme wet years occurred in the 20 th century (9 years, accounting for 60%).Among the extreme years, 1784, 1853, 1818, 1862 and 1863 were the five driest years, and 1998, 1952, 1770, 1993 and 1766 were the five wettest years (Table 5).We also found that many extreme dry or wet years occurred in succession.
Compared with the severe single-year droughts, multi-year droughts had the greatest effect on tree growth, and we further defined the dry and wet periods as those when the 11-year moving average PDSI was more than 0.5 SD from the mean for at least 2 consecutive years.Four dry periods, AD 1751-1752, 1812-1817, 1847-1866 and 1908-1927, and four wet periods 1757-1771, 1881-1902, 1952-1955 and 1989-2004 were identified (Table 5).The dry periods of 1847-1866 and 1906-1927 were the longest, spanning 20 years, while the longest wet period, from 1881-1902, lasted for 22 years (Table 5).The multiyear drought of 1847-1866 was the most serious due to long duration and intensity, and 1906-1927 was the second most significant drought (Table 6).Wet periods of 1757-1771 and 1989-2004 were the most remarkable in terms of intensity and duration (Table 6).

Climate-growth relationship
Scots pine is an extremely drought-tolerant species and drought stress is thought to be the main climate limitation for its radial growth in semi-arid or arid regions, such as in the Mongolia Plateaus and western Daxing'an Mountains (Bao  et al. 2015;Davi et al. 2006;Liu et al. 2009;Pederson et al. 2013).Previous dendroclimatic studies from these regions suggest that radial growth of Scots pine is sensitive to humidity, precipitation or drought (e.g.PDSI, SPEI), and most analyses have reconstructed hydroclimatic history (Bao et al. 2015;Liu et al. 2009).In these areas, the radial growth of Scots pine usually has a typical climatic (drought) response pattern with positive tree growth response to increasing precipitation and negative response to increasing temperature (Bao et al. 2015;Davi et al. 2006;Liu et al. 2009).This typical drought response pattern usually is found in other drought or wetness tree ring reconstructions (Li et al. 2016;Liu et al. 2016).In this study, the correlation between tree-ring indices and monthly precipitation and temperature data revealed that the radial growth of Scots pine was mainly limited by water, which is consistent with the physiological characteristics of tree species living in arid regions.A significant positive relationship between the tree-ring index and PDSI in all months supported moisture as the main limiting factor for radial growth of Scots pine (Fig. 4b).
A drought response was also found in Dahurian larch (Wang and Lv 2012), another important conifer tree species in the study area.However, the typical drought response to temperature was not obvious, and the radial growth of Scots pine was not significantly negatively correlated with growing season (July-September) temperature (Fig. 4a).On the contrary, a significant positive response of radial growth to winter (non-growing season) temperature was found, suggesting that higher winter and spring temperatures prolong the growth period and increased nutrient availability for trees during the summer (Hollesen et al. 2015;Zhu et al. 2017).This phenomenon might be due to the relatively humid climate and the northern latitude of our study sites, where the positive effect of temperature was greater than the negative effect resulting from drought stress (Wang and Song 2011).Similar drought response patterns were also found in tree-ring-based drought reconstructions in the middle Qilian Mountains (Sun and Liu 2013) and the Tienshan Mountains of western China (Chen et al. 2015).

Comparison with regional record
We used local historical records to verify our PDSI reconstruction for the timing of extreme dry years or periods.
During the last 260 years, 60.1% (13/22) of extreme dry years were noted in historical documents (Shen 2008;Sun 2007).Tree rings can not fully record the continuous drought events (years) resulting in a limited percentage or correspondence.For example, the extreme drought years of 1860-1865 were recorded only during 1861 in our reconstruction.Thus, some severe drought events affect radial tree growth in some but not all years (Fritts 1976).
Besides, the lag response of radial growth to climate (drought) might have a great contribution to unrecorded extreme drought events (Fritts 1976), for instance local historical documents record the dry years of 1817 and 1855 and these appear narrower rings in that year, or as an extreme dry event in the following year.Two multiyear droughts recorded in tree-rings, 1847-1866 and 1908-1927, can both be identified in historical documents (Shen 2008;Sun 2007).
Moreover, SEA between forest fire history and reconstructed drought variables revealed that a significant drop of PDSI  et al. 2010;Li et al. 2015;Liu et al. 2016).Therefore, our drought reconstruction is necessary to gain a thorough understanding of the East Asian Monsoon climate variability.
In a larger spatial scale, the streamflow reconstruction of Selenge River in the West-Central Mongolian Plateaus from Davi et al. (2006) presented a significant positive correlation with our drought reconstruction in low frequency (R L = 0.29**; p < 0.01) during the full periods.Our reconstructed PDSI also displayed some common variation trends or dry/wet periods with the reconstructed streamflow variations from the Selenge River (Davi et al. 2006), especially in the decadal scale.These relationships suggest that there are common drivers affecting the dry/wet variations of the Daxing'an Mountains and the West-Central Mongolian Plateau, although there might be some discordance.Among those differences, the most obvious is the completely different dry/wet variation trends among the Daxing'an Mountains (wetter), the West-Central Mongolian Plateaus (mild drier) as well as their transition zones: The East Mongolian Plateaus (Hulun Buir steppe; drier) since the late 1970s (Fig. 10a).Similar results were also found by Dai (2013), who presented a different dry-wet pattern under global warming using observations and models.In the Tibetan Plateau, Li et al. (2016) found moisture increase coherent to rapid warming (warm-wet).Although the reason for this divergence needs to be further studied, it might be related to the different response to the phase shift (negative to positive) of the  7, Fig. 10b).Wang et al. (2005) found a potential link between the Asian monsoon and solar changes.Local drought variations of the Daxing'an Mountains might be driven by solar activity that affects the Asian monsoon and influences local climate (temperature and precipitation) (Fig. 12).
Cycles of 46.5 -48.8 years might be related to the Pacific Decadal Oscillation (PDO), since it coincided with the 50-70 year cycle of PDO (Macdonald and Case 2005).This was verified by the strong connection between our drought reconstruction and annual SSTs over the Pacific Ocean (Fig. 11).The cycles/signals of PDO widely exist in most treering-based drought reconstructions (Bao et al. 2015;Chen et al. 2015;Sun and Liu 2013;Wang and Lv 2012), and many studies have confirmed that PDO can influence drought conditions in China (Bao et al. 2015;Cook et al. 2010;Ma 2007).The potential linkages between the PDO and local drought in the Daxing'an Mountains is further confirmed by the significant postive correlations between the PDO index (Mann and Lees 1996) and the dry-wet index of the Daxing'an Mountains both in low and high frequencies (Table 7, Fig. 10b).The positive/warm phase of the PDO index usually corresponds with drought periods, and the PDO negative/cold phase often matches wet periods (Ma 2007).For example, the severe drought of the 1920s -1930s corresponds to the PDO negative phase.The PDO might drive the dry/wet variations of the Daxing'an Mountains by modifing the intensity or location of Asia Monsoon (Bao et al. 2015;Cook et al. 2010;Ma 2007).Significant postive correlations between the PDO index and local climate (temperature and preciptation) were also found, revealing that the PDO may affect the dry/wet variations of the Daxing'an Mountains by regulating the Asian monsoon to affect local temperature or precipitation (Fig. 12).Similar results were found in a nearby tree-ring-based drought reconstruction ( (Bao et al. 2015).
Ultimately, the cycles around 73-years might result from oscillatory changes in the North Atlantic SST, which has a period of 60-90 years (Knudsen et al. 2011).Spatial correlations between our drought reconstruction and annual SSTs also show a stong teleconnection over the Atlantic Ocean (Fig. 11), which further confirmed potential linkages between the North Atlantic SSTs and dry-wet cycles in the Daxing'an Mountains.Although our study area is far away from the Atlantic Ocean, many studies have confirmed that large-scale climate oscillations in the Atlantic Ocean (such as the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO) as well as Summer NAO (SNAO)) could West-Central Mongolia (Davi et al. 2006), and northwest China (Chen et al. 2015;Sun and Liu 2013).Forthmore, we also identified significant negative/postive correlation between the dry-wet change of the Daxing'an Mountains (Zscore) and the AMO, NAO and SNAO index both in low or high frequency (Table 7, Fig. 10c).The strong AMO signal (Wang et al. 2011) and teleconnections with SNAO (Linderholm et al. 2013) also have been found in tree-ring widths of Scots pine in northeast China and eastcentral Siberia during the last 400 years.All of these studies confirmed that oscillatory changes of the North Atlantic SST (e.g.AMO, NAO and SNAO) could drive dry-wet changes in the Daxing'an Mountains.Although its mechanism needs to be further studied, the close relationship between the oscillatory changes of North Atlantic SST and the Asian monsoon has been demonstrated.Recent studies have shown that the AMO (Wang et al. 2013), NAO (Feng and Hu 2008) and SNAO (Linderholm et al. 2011) all have the potential to drive or affect the Asian monsoon.In this study, although only the AMO index was significantly correlated with local climate (temperature and precipitation) (Table 7, Fig. 10c), it also confirmed that the oscillatory changes of North Atlantic SST, especially the AMO, could drive wet-dry changes in the Daxing'an Mountains by influening the Asia Monsoon (local temperature and precipitation, Fig. 12) (Bao et al. 2015;Chen et al. 2015;Cook et al. 2010;Li et al. 2015;Linderholm et al. 2011;Sun et al. 2008).

Conclusion
In this study, we developed a 260 years (1751 to 2010) tree-ring chronology for Scots pine (Pinus sylvestris L. var.b Rbar = the mean correlation coefficient between all tree-ring series used in a chronology. Clim.Past Discuss., https://doi.org/10.5194/cp-2018-31Manuscript under review for journal Clim.Past Discussion started: 27 March 2018 c Author(s) 2018.CC BY 4.0 License.
regional forest fire event lists (Mengkeshan and Pangu; Fig. 1) reconstructed by tree-ring scars in nearby forest were used (Yao et al. 2017) and the SEA were carried out using software FHAES V2.0.0 (https://www.frames.gov/partnersites/fhaes/download-fhaes/).In addition, the consistency between our reconstruction and other local drought related time series including the gridded Standardised Precipitation-Evapotranspiration Index (SPEI), the Monsoon Asia Drought Atlas (MADA) from Cook et al. (2010) (Cook-PDSI, hereafter) and Self-calibrating PDSI from Schrier et al. (2013) (scPDSI, hereafter), and nearby tree-ring-based hydroclimatic reconstructions (the December-March precipitation reconstruction of the A'li River (AR) in the Daxing'an Mountain from Lv and Wang (2014), the April-August SPEI reconstruction of the Hulun Buir steppe (HB) on the east edge of Mongolian Plateaus from the western Clim.Past Discuss., https://doi.org/10.5194/cp-2018-31Manuscript under review for journal Clim.Past Discussion started: 27 March 2018 c Author(s) 2018.CC BY 4.0 License.analysis.Teleconnections between reconstructed drought variables and global sea surface temperature (0.5°×0.5°) were carried out to verify the potential drivers of large-scale climate on local drought.

Clim.
Past Discuss., https://doi.org/10.5194/cp-2018-31Manuscript under review for journal Clim.Past Discussion started: 27 March 2018 c Author(s) 2018.CC BY 4.0 License.valuesoccurred during the year of the forest fire in Mengkeshan and Pangu (Fig.8), further validating the accuracy of our reconstruction.Spatial correlation analysis indicated a strong correlation pattern between our reconstruction and gridded scPDSI in Northeast Asia (Fig.9), and our reconstruction also represents drought/wet variations in surrounding geographic regions.During the common periods, our reconstruction shares a similar dry/wet fluctuation with the precipitation of A'li River and SPEI of Hulun Buir steppe both in the low and high frequency (Fig.9b-d).Significant (p < 0.05) correlations among them were found in low and high frequency and some common dry/wet periods were highlighted in Figure9, which confirmed that our drought reconstruction could almost fully account for the dry/wet variations of the Daxing'an Mountains, northeast China.It's important to note that our drought reconstruction and the MADA of "Cook" from the same PDSI grid was not consistent and showed a completely opposite trend (R L = -0.19**;p < 0.01) in low frequency (Fig.9).Negative correlations between the MADA and the SPEI (R L = -0.311*;p = 0.03) and scPDSI (R L = -0.126;p = 0.236), positive correlations between our drought reconstruction and the SPEI (R L = 0.950**; p < 0.01) and scPDSI (R L = 0.807**; p < 0.01) were also found, although it has a seasonal difference with our drought reconstruction.These both imply that MADA of "Cook" might be inaccurate or even reversed in characterizing dry/wet variations in the Daxing'an Mountains.Similar divergence of tree-ring-based drought reconstruction between the MADA and individual sampling sites was also found byLi et al. (2015) from Guancen Mountain andLiu et al. (2016) from central Inner Mongolia.The insufficient spatiotemporal distribution of tree-ring network, especially in eastern China, used by MADA might be the main reason for this divergence/inaccuracy (Cook

Clim.
Past Discuss., https://doi.org/10.5194/cp-2018-31Manuscript under review for journal Clim.Past Discussion started: 27 March 2018 c Author(s) 2018.CC BY 4.0 License.The 12.05-12.33-yearcycles were close to the 10-to 12-year activity cycle indicating that dry/wet variations in the Daxing'an Mountains might be controlled by solar activity (Shindell et al. 1999).Many previous studies have demonstrated that solar activity can drive local dry-wet variations (Chen et al. 2015; Hodell et al. 2001; Sun and Liu 2013).In northeastern China, Hong et al. (2001) also found the signals of solar activity in a 6000-year record of drought and precipitation.Significant postive correlations between the Total Solar Irradiance (TSI; reconstruction from IPCC AR5) and the dry-wet index (averaged Z-scores) of the Daxing'an Mountains both in low and high frequencies, and between the TSI and local climate (temperature and preciptation) further confirmed the relationship between solar activity and local drought (Table

mongolica
Litv.) from four sample sites of the Daxing'an Mountains, in northeast China.Using a significant correlation between the tree-ring index and annual Dai-PDSI (R = 0.62, p < 0.01), we reconstructed a new annual PDSI record for the Daxing'an Mountain that explains 38.2 % of the PDSI variance during the period 1911-2010.Four dry and wet periods were found during the past 260 years.The extreme dry years in our reconstruction are consistent with local historical records and nearby forest fire history.Results show that our reconstruction not only accounted for the dry and wet variations for the Daxing'an Mountains, but also are representative of the West-Central Mongolian Plateaus, especially at the decadal scale.Drought of 1920s-1930s in the Daxing'an Mountains was more severe than in surrounding areas.Moreover, there has been obvious warming and wetting since the late 1970s, which is distinct from events that occurred on the Mongolian Plateaus, especially in its transition zones.The MADA of "Cook" might be inaccurate or even reversed in referring to the dry/wet variations in the Daxing'an Mountains, which might be due to insufficient spatiotemporal distribution of the tree-ring network in eastern China.Overall, the dry/wet variability of the Clim.Past Discuss., https://doi.org/10.5194/cp-2018-31Manuscript under review for journal Clim.Past Discussion started: 27 March 2018 c Author(s) 2018.CC BY 4.0 License.

Fig. 7 :Fig. 11 :Fig. 12 :
Fig. 7: Multi-taper method power spectrum of reconstructed Dai-PDSI for the period AD 1751-2010.The 95% and 99% confidence level relative to red noise are shown and the numbers refer to the significant period in years.

Table 1
Information of the weather stations and gridded data nearest to sampling sites.Clim.Past Discuss., https://doi.org/10.5194/cp-2018-31Manuscript under review for journal Clim.Past Discussion started: 27 March 2018 c Author(s) 2018.CC BY 4.0 License.

Table 2
Site description and statistical characteristic for the Pinus sylvestris chronologies in the Daxing'an Mountains.
a Expressed population signal statistic.

Table 3
Five-chronology correlation matrix over the common period 1793-2010.

Table 5
Reconstructed extreme dry/wet years and annual PDSI of the Daxing'an Mountains.

Table 7
Correlation coefficients between large-scale climate and local annual mean temperature, total precipitation, actual Dai-PDSI as well as the Z-score of dry/wet variation among the Daxing'an Mountains (DMZ-score)