Effect of precipitation seasonality on annual oxygen isotopic composition in the area of spring persistent rain in southeastern China and its palaeoclimatic implication

This study examines the seasonality of precipitation amount and δO over the monsoon region of China (MRC). We found that the precipitation amount associated with the East Asian summer monsoon (EASM) in the spring persistent rain (SPR) region is equivalent to that of the non-summer 25 monsoon (NSM). The latter contributes ~50% to amount-weighted annual δO values, in contrast with other areas in the MRC, where the δO of annual precipitation is dominated by EASM precipitation. Interannual relationships between the ENSO index, simulated δO data from IsoGSM, and seasonal precipitation amount in the SPR region were also examined. We found that on interannual timescales, the seasonality of precipitation amount (EASM/NSM ratio) was modulated by ENSO and primarily 30 influences the variability of amount-weighted annual precipitation δO values in the SPR region, although integrated regional convection and moisture source and transport distance may also play subordinate roles. During El Niño (La Niña) phases, less (more) EASM and more (less) NSM precipitation leading to lower (higher) EASM/NSM precipitation amount ratios results in higher (lower) 2 amount-weighted annual precipitation δO values and, consequently, in higher (lower) speleothem δO 35 values. Characterizing spatial differences in seasonal precipitation is, therefore, key to correctly interpreting speleothem δO records from the MRC.

Although considerable emphasis has been placed on understanding the causes and mechanisms of SPR, little is known about its precipitation δ 18 O (δ 18 Op) variability and about the mechanisms that 80 produce this variability (Tan, 2016;Zhang, 2014). Based on rainfall monitoring data from eight sites in the EASM region, Tan et al. (2016) found that, in 2012 AD, the spring rainfall amount was equivalent to the summer rainfall but their δ 18 Op values were different. They suggested that the seasonal δ 18 Op variability is affected by the changes of moisture source but not the precipitation amount variations. Huang et al. (2017) and Wu et al. (2015) studied the δ 18 Op variability at the Changsha station located in 85 the SPR region and its relationship with the El Niño-Southern Oscillation (ENSO) (Figure 1a), but did not focus on the δ 18 Op variability of SPR. A better understanding of the δ 18 Op variability in the SPR region on seasonal to interannual time scales, however, is crucial for a robust interpretation of the oxygen isotopic data of Chinese speleothems from this region (e.g., Cai et al., 2015;Cheng et al., 2009Cheng et al., , 2016Wang et al., 2001Wang et al., , 2008Yuan et al., 2004;Zhang et al., 2008). Several mechanisms including the amount 90 4 effect, moisture source/transport distance, integrated regional convection, winter temperature, and precipitation seasonality have been shown to influence the δ 18 Op and speleothem δ 18 O to various degrees and at different timescales across the MRC Caley et al., 2014;Cheng et al., 2016;Clemens et al., 2010;Dayem et al., 2010;Maher, 2008Maher, , 2016Maher and Thompson, 2012;Pausata et al., 2011;Tan, 2016;Zhang et al., 2018). The SPR region is located within the area of the EASM and its 95 rainy season includes both summertime monsoon rainfall and SPR . Therefore, the factors that influence the δ 18 Op in this region are likely complex. The aim of this study is to examine this climate-δ 18 O proxy relationship during the instrumental period. To this end, we compare the seasonal variations of precipitation amount and δ 18 Op in the SPR region with other regions of the MRC and discuss the interannual variations of precipitation amount and δ 18 Op in the SPR region and their relationship with 100 the large-scale oceanic-atmospheric circulation.

Meteorological data
A daily gridded precipitation dataset for 1951-2007 was obtained from APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources, 105 APHRO_MA_V1101R2 product, (21)) (Yatagai et al., 2009). The regional mean SPR (March-April) and EASM (May-September) precipitation amount in China from 1951 to 2007 are shown in Figure 1, which was exported based on this dataset using the free software Ferret (https://ferret.pmel.noaa.gov/Ferret).
Monthly precipitation datasets of 160 meteorological stations in China for the period 1951-2014, obtained from the National Climate Center (http://ncc.cma.gov.cn), were used to characterize the 110 percentage of spring (March-April) and EASM (May-September) precipitation amount relative to the annual precipitation amount in China.
Monthly mean δ 18 Op and precipitation amount data from meteorological stations across the MRC were obtained from the Global Network for Isotopes in Precipitation (GNIP) (http://www.iaea.org/) (Table 1 and Figure 1a). The monthly mean δ 18 Op data are used to compare the seasonal to interannual 115 variation of δ 18 Op in the MRC. The stations near the coast from the southeastern region of the MRC (Fuzhou, Haikou, Hongkong, Guangzhou) were excluded, because their precipitation amount and δ 18 Op are significantly influenced by typhoons in summer and autumn. Changsha station is the only GNIP station in the SPR region.

δ 18 Op data from IsoGSM simulations 120
IsoGSM is a water isotope-permitting general circulation model (Yoshimura et al., 2008). We use the product of IsoGSM nudged toward the NCEP/NCAR Reanalysis 2 (Kanamitsu et al., 2002) atmosphere and forced with observed sea-surface temperatures (SST) and sea ice data (Yoshimura et al., 5 2008). A detailed description of the model setup can be found in Yoshimura et al. (2008) and Yang et al. (2016). IsoGSM can reproduce reasonably well monthly variabilities of precipitation and water vapor 125 isotopic compositions associated with synoptic weather cycles. In order to verify the reliability of the simulated data from the IsoGSM, we first cross-compare the data from GNIP Changsha station with those from the IsoGSM during 1988-1992. The good replication indicates that both the precipitation amount and the δ 18 O data from the IsoGSM simulation are consistent (Supplementary Figure 1).

Ocean-atmosphere circulation index 130
ENSO plays an important role in governing the climatic variation in the MRC (e.g., Feng and Hu, 2004;Xue and Liu, 2008;Zhou and Chan, 2007). We used the Southern Oscillation Index (SOI) multivariate ENSO Index (MEI) to calculate the correlations between the phases of ENSO, the δ 18 Op and the seasonal precipitation amount. The SOI is defined as the normalized pressure difference between Tahiti and Darwin. Negative and positive values of SOI represent El Niño and La Niña events, 135 respectively. The data were obtained from the Australian Government Bureau of Meteorology (http://www.bom.gov.au/climate/current). The MEI is based on six oceanic-atmospheric variables (sealevel pressure, zonal and meridional components of the surface wind, SST and total cloudiness fraction of the sky) over the tropical Pacific, and is used to examine the role of ENSO in influencing the rainfall over the MRC. The MEI is defined as the first principal component of above six variable fields. Therefore, 140 it provides a more complete description of the ENSO phenomenon than a single variable ENSO index such as the SOI or Niño 3.4 SST (Wolter and Timlin, 2011 The Arctic Oscillation (AO) can also influence the climate and precipitation over the MRC (Gong 150 et al., 2001(Gong 150 et al., , 2011He et al., 2017;Li et al., 2014). It was suggested that a warmer winter in East Asia (a positive winter AO value) is associated with increased winter rainfall in southern parts of East Asia, and a positive spring AO is followed by increased rainfall in southern China but decreased rainfall in the lower valley of Yangtze River (He et al., 2017). We also calculated the correlation between the AO index and the seasonal rainfall amount in our study area. The data were downloaded from the website of NOAA 155 (https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao.shtml#forecast).

2.4 Back-trajectory and moisture source contribution calculation
The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) (Stein et al., 2015) was used to perform air mass back trajectory calculations for the GNIP Changsha station during the period 1988-1992. In order to qualitatively assess the moisture source regions and transport paths for 160 rainy season precipitation only air mass back trajectories for precipitation-producing days were used.
Trajectories were initiated four times daily (at UTC 00:00, 00:60, 12:00, and 18:00) during precipitating days (>1 mm precipitation/day) and their air parcel was released at 1500 m above ground level and moved backward by winds for 120 hours (5 days). To identify the moisture uptake locations along the back trajectories during 1988-1992, we followed the method described in Sodemann et al. (2008) and Krklec 165 et al. (2018). Two criteria (i.e., a more conservative threshold of positive gradient in specific humidity (0.2 g/kg within 6 h), and initial relative humidity of more than 80%) were used to identify moisture uptake locations along the back trajectories. Following the methodology of Krklec et al. (2018), we calculated the contributions of moisture uptake locations en route to the precipitation in GNIP Changsha station and provide a map showing the percentage of moisture uptake contributing to Changsha 170 precipitation during La Niña (1988)(1989) and El Niño (1991Niño ( -1992 years. A grid of 0.5×0.5 degrees was used for the computation of the moisture uptake locations.

Proportions of SPR, EASM and NSM precipitation over MRC
We calculated the mean ratios of spring (March-April) to annual precipitation (spring/annual) and 175 EASM (May-to-September) to annual precipitation (EASM/annual) ratios for the period 1951-2014. Hunan Provinces, the core regions of the SPR, show the highest mean percentage of spring/annual within the MRC (20-25%). which is consistent with the results from the previous studies (Tian and Yasunari, 1998;. Figure 2b shows that the mean percentage of EASM/annual is 40-70% in southeastern China and 70-95% in other regions of the MRC. Conversely, the mean percentage of nonsummer monsoon precipitation to annual precipitation (NSM/annual) is 30-60% in southeastern China 185 and 5-30% in other regions of the MRC and reaches the maximum in the SPR region (45-60%). This indicates that the proportion of EASM precipitation (40-55%) is nearly equivalent to the proportion of NSM precipitation (45-60%) in the SPR region. Usually, the SPR period lasts from March to mid-May  and the EASM period lasts from mid-May to September (Wang and Lin, 2002), however, the onset/retreat time of SPR and 200 EASM and their intensities vary in different years (Zhou and Chan, 2007). The EASM starts late (late May to early June) and tends to be weaker during El Niño years (Huang et al., 2012) and EASM precipitation amount over Southeast China is reduced when the SPR starts later and lasts longer time (until late May) (Wan et al., 2008a). Therefore, if we define the March-to-May precipitation as SPR and the June-to-September precipitation as EASM in El Niño years, the mean percentage of SPR/annual in 205 the SPR region is 35-45% (Figure 2c), the mean percentage of EASM/annual is only 30-40% (Figure 2d), 8 and the mean percentage of NSM/annual is 60-70%. However, in other regions of the MRC, the mean percentage of EASM/annual (65-90%) is still much higher than the mean percentage of NSM/annual (10-35%) (Figures 2c and d). Conversely, during La Niña years, the March-April and May-to-September precipitation should be defined as SPR and EASM precipitation, respectively (Figures 2a and b). 210 Therefore, the distribution of EASM vs. NSM precipitation amount in the SPR region is distinctly different from that in other regions of the MRC, and the ratio of EASM/NSM precipitation amount in the SPR region might be influenced by ENSO. We discuss this in detail in the section 4.2.

Seasonal precipitation δ 18 Op and amount over the MRC
We compared the seasonal variations of precipitation amount and δ 18 Op in the SPR region with those 215 in other regions of the MRC by using data from the GNIP stations. According to the spatial distribution of EASM precipitation as discussed in section 3.1, we assigned Zhengzhou, Xi'an, Yantai, Shijiazhuang, and Tianjin GNIP stations to northern region of the MRC, Kunming, Guiyang, Zunyi, and Chengdu GNIP stations to southwestern region of the MRC, and Changsha, Guilin, Liuzhou, Nanjing, and Wuhan GNIP stations to southeastern China. Only the Changsha GNIP station is located in the SPR region (Table  220 1 and Figure 1a). The seasonal variation of δ 18 Op in the MRC is consistently related to the onset, advancement, and 225 retreat of the EASM. The δ 18 Op values decrease in May as the summer monsoon starts (Figure 3). The δ 18 Op values are relatively low during the monsoon season (June-August) (Figure 3), because of the longdistance transport of water vapor from the distal Indian Ocean to the MRC. Along this pathway, progressive rainout associated with regional convection leads to more negative δ 18 Op values via Rayleigh distillation (Baker et al., 2015;He et al., 2018;Liu et al., 2010;Moerman et al., 2013;Tan, 2014). The 230 δ 18 Op values become progressively higher as the EASM withdraws in September (Figure 3). From October to next April, the δ 18 Op values are rather high (Figure 3), resulting from the short-distance transport of water vapor from the western Pacific Ocean or local moisture recycling and local convection (He et al., 2018;Moerman et al., 2013;Tan et al., 2016;Wu et al., 2015). The low δ 18 Op values in winter in northern region of the MRC are caused by the temperature effect, but it is less important because of 235 its small contribution to the amount-weighted mean annual precipitation δ 18 O (δ 18 Ow) (Cheng et al., 2012).  (Figures 4a and c). In previous studies researchers mainly focused on the variations in moisture source during the EASM season (Baker et al., 2015;Cai et al., 2017;Tan, 2014). In this study, 265 however, we also analyzed the back-trajectories during the NSM season because NSM precipitation contributes ~50% to the annual precipitation in the SPR region. It shows that the NSM moisture sources originate from South China Sea and South China, the remaining ones are driven from local evaporation.  where PEASM-mean and PNSM-mean are the mean precipitation amounts of EASM and NSM, δ 18 OEASMmean and δ 18 ONSM-mean are the mean values of EASM and NSM precipitation, and EASM% and NSM% are the mean percentages of the EASM and NSM precipitation amounts, respectively. Therefore, we can consider that the δ 18 Ow is controlled by both precipitation amount and δ 18 Op during the EASM and NSM seasons in the MRC. Given the relationship between monthly precipitation 290 amount and δ 18 Op in the MRC (Figure 3) we find that: 1) In northern and southwestern regions of the MRC δ 18 Ow are mainly controlled by the amount and δ 18 O of EASM precipitation, because the precipitation amount of the EASM with rather low δ 18 Op values accounts for 70% of the annual precipitation and the NSM precipitation is only a small contribution to the δ 18 Ow (less than 30%). 2) In Southeast China, especially in the SPR region, the precipitation amount of the NSM with rather high 295 δ 18 Op values even exceeds that of the EASM with rather low δ 18 Op values, and it also has an important effect on δ 18 Ow. Hence, δ 18 Ow in the SPR region is affected by both EASM and NSM precipitation. In addition, except for the effect of the seasonal distribution of precipitation amount, the seasonal δ 18 O itself also attributes to the δ 18 Ow, which is related, among others, to the variations in integrated regional convection and moisture source and transport distance Baker et al., 2015;Huang et al., 300 2017;Tan et al., 2016).
In order to separate the influences of precipitation seasonality and monthly δ 18 Op, we used the decomposition method used by Liu and Battisti (2015) and Cai and Tian (2016) to evaluate the role of changes in precipitation seasonality (δ 18 Ops; assuming that the monthly precipitation δ 18 Op in El Niño years 1988-1989 is the same as that in La Niña years 1991-1992). We then calculated the difference 305 between precipitation δ 18 Ow in El Niño years and La Niña years and the change in precipitation δ 18 O (δ 18 Oiso; method is similar to that for calculating δ 18 Ops but assuming that the monthly precipitation amount is the same). The results for the Changsha station indicate that the difference in precipitation δ 18 Ow between El Niño years (1988)(1989) and La Niña years (1991-1992) (i.e., El Niño minus La Niña) is 2.7‰, δ 18 Ops is 1.3‰, and δ 18 Oiso is 1.3‰. These results imply that the difference in δ 18 Ow between El 310 Niño and La Niña conditions reflects the differences of both the δ 18 Op and the precipitation seasonality.
Tan (2014)  that the moisture sources vary little between years with relatively high and low δ 18 O values (corresponding to El Niño and La Niña years) in the EASM region; hence EASM precipitation is primarily derived from the Indian Ocean, while the Pacific Ocean moisture is a minor contributor. This is consistent with our results (Figure 4). In addition, by using a Lagrangian precipitation moisture source diagnostic, Baker et al. (2015) suggested that the moisture uptake area in the Pacific Ocean does not 320 differ significantly between summer and winter and is thus a minor contribution to monsoonal 13 precipitation; changes in moisture transport, however, may impact the δ 18 O variation of EASM precipitation. Dayem et al. (2010) also proposed that several processes (e.g., source regions, transport distance and types of precipitation) contribute to the δ 18 Op variation. We found that the moisture sources in the Bay of Bengal-Indochina Peninsula and the Indian Ocean were less important during the NSM 325 season compared to the EASM season (Figure 4). The moisture uptake area in the EASM season does not differ significantly between El Niño and La Niña years nor in the NSM season. Their contributions to the whole precipitation in El Niño and La Niña years, however, are different (Figure 4). The variation in moisture source during the EASM period, in some extent, might contribute to changes in δ 18 Ow, but it is not the main factor. We also emphasize the effect of NSM precipitation amount on δ 18 Ow in the SPR 330 region and we made an attempt to analyze the relationship between the seasonal precipitation amount and δ 18 Ow with ENSO phase on interannual timescale in the next section.

Interannual variation of precipitation amount and δ 18 Ow over the SPR region influenced by ENSO
The ENSO is a coupled oceanic-atmospheric phenomenon controlling the interannual variation in precipitation amount and δ 18 O over southeastern China (e.g., Feng and Hu, 2004;He et al., 2018;Huang 335 et al., 2017;Moerman et al., 2013;Tan et al., 2014;Xue and Liu, 2008;Yang et al., 2016). Our analysis of the 1988-1992 data from the Changsha GNIP station suggests that the mean value of δ 18 Ow (-6.73‰) in La Niña years (1988)(1989) is significantly more negative than during El Niño years (1991-1992; -4.11‰). However, there is no significant variation in the annual precipitation amount between La Niña and El Niño years (Figure 5a). The difference of δ 18 Ow between La Niña and El Niño phases cannot be 340 explained by variations in annual precipitation amount. This is consistent with the analyses based on instrumental meteorological data (Huang et al., 2017;Tan, 2014) and climate simulations (Yang et al., 2016). Previous studies showed that during El Niño years, the EASM is generally weak and the integrated regional convection decreases in the EASM region, thereby leading to higher δ 18 Op values, while the effect of La Niña is opposite Gao et al., 2013;Zwart et al., 2016). Continental moisture 345 recycling or local convection during the NSM season has limited impact on δ 18 Op relative to the integrated regional convective activities during the EASM season. As we discussed in section 4.1, however, the difference in δ 18 Ow between El Niño and La Niña years is influenced by both the δ 18 Op and the precipitation seasonality. Indeed, there is more summer monsoon precipitation in June-to-September during La Niña years (1988)(1989) but more SPR in March-April during El Niño years (1991)(1992), 350 though the annual precipitation amounts are similar (Figure 5b). We find that the δ 18 Ow variability is broadly consistent with the variation in the ratio of EASM/NSM precipitation amount during 1988-1992 ( Figure 5). Unfortunately, the data series of the Changsha GNIP station is too short (5 years) to evaluate the relationship between the EASM/NSM ratio and δ 18 Op in the SPR region. Therefore, we used the average precipitation data from 11 meteorological stations  in the Jiangxi Province and the 355 eastern Hunan Province (Fig. 2, i.e. from the core area of the SPR) and the δ 18 O data obtained from the 14 IsoGSM simulation  to examine the relationship between ENSO, AO, δ 18 Ow and precipitation amount in the SPR region on interannual timescales.  (1988)(1989) and El Niño (1991Niño ( -1992 years. In this calculation, the temporal coverage of the annual precipitation and the precipitation δ 18 Ow is from January to December, the EASM precipitation is from May to September and the NSM precipitation is from January to April and from October to December. 365 We calculated correlation coefficients between the simulated δ 18 Ow, the SOI, the MEI, the EASM/NSM ratio, and the annual, EASM, and NSM precipitation amounts for 1979-2009 (Table 2 and Figure 6). The results show that the time series of the simulated δ 18 Ow data significantly correlates with the SOI (r=-0.52, p<0.01) and the MEI (r=0.51, p<0.01), consistent with the positive relationship between the ENSO index and δ 18 Ow observed in modern precipitation (Huang et al., 2017;Tan, 2014;370 Yang et al., 2016) as well as in the δ 18 O records of speleothems and tree-ring cellulose (Tan, 2016;Xu et al., 2013Xu et al., , 2016aXu et al., , 2016bZhang et al., 2018). Furthermore, the same relationship holds for the Changsha GNIP station (Figure 5a). This indicates that the precipitation δ 18 Ow is higher (lower) during the El Niño (La Niña) phase in the SPR region. There is, however, no significant correlation between the δ 18 Ow with the annual, EASM or NSM precipitation amount. This indicates that on interannual timescales, the δ 18 Ow 375 is not controlled by the annual or EASM precipitation amount in Southeast China, consistent with the 15 result based on instrumental data from the Changsha station (Figure 5a) and other studies Yang et al., 2016). The time series of the simulated δ 18 Ow data correlates with the EASM/NSM ratio (r=-0.36, p<0.05) ( Figure 6 and Table 2) suggesting that the precipitation δ 18 Ow may be influenced by the precipitation seasonality (i. e., EASM/NSM ratio) modulated by ENSO on interannual timescales. 380 Applying a 2-year smoothing, the time series of the simulated δ 18 Ow data significantly correlates with the annual precipitation (r=-0.89, p<0.01), the EASM precipitation (r=-0.91, p<0.01) and the EASM/NSM ratio (r=-0.81, p<0.01) (Table 2 and Figure 6). This indicates that on interannual to decadal timescales the precipitation δ 18 Ow might reflect changes in EASM precipitation amount and also the annual precipitation amount and the EASM/NSM ratio, because the EASM/NSM ratio and annual 385 precipitation amount are significantly dominated by the EASM precipitation amount (Table 2). Applying a 2-year smoothing δ 18 Ow is significantly correlated with annual precipitation (r=-0.89, p<0.01), EASM precipitation (r=-0.92, p<0.01) and the EASM/NSM ratio (-0.81, p<0.01).
395 Table 2. Correlation coefficients between the time series of precipitation δ 18 Ow, MEI, the EASM/NSM ratio, and the annual, EASM, and NSM precipitation amount in the SPR region for 1979-2009. * indicates significant correlation at the 0.05 level (2-tailed). ** indicates significant correlation at the 0.01 level (2tailed). The temporal coverage of the annual precipitation and the precipitation δ 18 Ow is from May to next April, the EASM precipitation and the NSM precipitation is from May to September and from 400 October to next April. The temporal coverage of the MEI and the SOI is from October to April. To explore the relationship between ocean-atmospheric circulation (e.g., ENSO, AO) and the seasonal precipitation amount, we calculated correlation coefficients between the SOI, MEI, AO index, the EASM/NSM ratio, and the annual, EASM, and NSM precipitation amount for 1951-2010 (Table 3). 405 The mean value of October-next June SOI correlates with the EASM precipitation amount (r=0. 26, p<0.05), NSM precipitation amount (r=-0.51, p<0.01) and the EASM/NSM ratio (r=0.52, p<0.01) ( Table 3). The mean value of October-next June MEI correlates with the EASM precipitation amount (r=-0.29, p<0.05), NSM precipitation amount (r=0.54, p<0.01) and the EASM/NSM ratio (r=-0.55, p<0.01) ( Table 3). This indicates, on interannual timescales, decreased EASM precipitation during the 410 developing stage of El Niño and increased NSM precipitation during the mature stage of El Niño, resulting in lower EASM/NSM ratios during El Niño phases and vice versa. There is, however, no significant correlation between the SOI, MEI and the annual precipitation amount. In addition, the EASM/NSM ratio significantly correlates with the EASM (r=0.64, p<0.01) and the NSM (r=-0.70, p<0.01) (Table 3). 415 Previous studies found that decreased summer rainfall in the south of the Yangtze River occurs during the developing stage of El Niño, resulting from a southward shift of the subtropical high associated with colder SST in the western tropical Pacific and weak convective activities in the South China Sea and the Philippines (Huang and Wu, 1989;Zhang et al., 1999). Kong and Tu (2003) found that there is less EASM rainfall in May-September in the lower reaches of the Yangtze River valley during 14 El 420 Niño events since the 1950s. The same relationship is observed in the May-October rainfall reconstruction based on tree-ring cellulose δ 18 O (Xu et al., 2016a) during El Niño phases. Cooler summer SST in the western Pacific led to a weakened western Pacific Subtropical High resulting in less rainfall during May-October in the middle to lower reaches of the Yangtze River (Liu and Li, 2011;Xu et al., 2016a), and vice versa. Increased rainfall in autumn, winter and spring (i.e. NSM) occurred in southern 425 China during the mature stage of El Niño (Wan et al., 2008b;Wang et al. 2000;Zhang et al., 1999;Zhang et al., 2015;Zhou, 2011;Zhou and Wu, 2010). During these phases, lower-level southwesterly anomalies over the South China Sea transport more moisture into Southeast China, leading to increased NSM precipitation (Wang et al., 2000;Zhang et al., 1999;Zhou, 2011;Zhou and Wu, 2010). These conclusions are consistent with our findings in the SPR region. It is notable that there is no significant variation in 430 EASM precipitation amount in our study area during the decaying stage of El Niño, although increased summer rainfall was observed in southern China (Huang and Wu, 1989).
We also find that the May AO index significantly and negatively correlates with the annual (r=-0.42, p<0.01) and EASM (r=-0.39, p<0.01) precipitation amount in the SPR region (Table 3). This is consistent with previous observations that the positive May AO index is followed by decreased summer 435 precipitation amount in the lower Yangtze River valley (Gong and Ho, 2002;He et al., 2017). It was suggested that a stronger May AO is associated with a northwards movement of the summer jet stream, leading to drier conditions in the lower Yangtze River. The positive spring AO gives rise to warmer equatorial SSTs between 150°-180° E and weakens summer subtropical high in the western North Pacific.
Consequently, decreased summer precipitation occurs in the lower Yangtze River (Gong et al., 2011). 440 There is, however, no significant correlation between the AO index and the NSM precipitation amount and the EASM/NSM ratio in the SPR region (Table 3). This might be because the influence of AO on the winter climate varied spatially and temporally, resulting from the unstable relationship between the AO index and the East Asian winter monsoon (He et al., 2017;Li et al., 2014). This indicates that the AO mainly influences the changes in EASM and annual precipitation amount but not the precipitation 445 seasonality (i. e., EASM/NSM ratio) in the SPR region. The February AO index positively correlates with precipitation amount in February (r=0.28,p<0.05).
Given the relationship between δ 18 Ow, SOI, MEI, and seasonal precipitation amount, we find that less EASM during the developing stages of El Niño and more NSM precipitation during the mature stages of El Niño lead to lower EASM/NSM ratios, resulting in higher δ 18 Ow values in the SPR region during 450 El Niño phases, and vice versa. We therefore suggest that, over the SPR region the precipitation seasonality (i.e., the EASM/NSM ratio) modulated by ENSO primarily influences the interannual variability of δ 18 Ow. The AO mainly influences changes in EASM and annual precipitation amount, but not the precipitation seasonality (i. e., EASM/NSM ratio) in the SPR region.  Figure 1 illustrates that (1) Wanxiang , Dayu (Tan et al., 2009), Huangye (Tan et al., 2010), Wuya , Shihua , and Xiaobailong  caves are located in the northern and southwestern part of the MRC, where δ 18 Ow is 470 primarily controlled by the EASM and annual precipitation amount. Therefore, these records show a significant correlation with the instrumental precipitation and the regional drought/flood (D/F) index obtained from historical documents (e.g., Li et al., 2017;Liu et al., 2008;Tan et al., 2009Tan et al., , 2010Tan et al., , 2014Tan et al., , 2017Zhang et al., 2008). (2) Dongge (Yuan et al., 2004), Heshang (Hu et al., 2008), Hulu (Wang et al., 2001), Yuhua (Jiang et al., 2012) and E'mei (Zhang et al., 2018) caves are located in Southeast China, 475

455
where δ 18 Ow is not only affected by EASM precipitation but also by NSM precipitation. Hence, according to Wang et al. (2001), speleothem δ 18 O from Hulu cave reflects the ratio of summer to winter precipitation amount. Factors related to the NSM (e.g., moisture source, integrated regional convection, precipitation seasonality, winter temperature) have also been taken into consideration in the interpretation of speleothem δ 18 O in Southeast China (e.g., Baker et al., 2015;Cai et al., 2018;Cheng et al., 2016;480 Clemens et al., 2010;Dayem et al., 2010;Zhang et al., 2018). On the other hand, the high percentage of NSM precipitation with relatively high δ 18 Op values in Southeast China should be an important reason why δ 18 Ow and speleothem δ 18 O are much lower and their variability is much larger in southwestern China than in Southeast China Liu et al., 2010;Zhang et al., 2018), except for the influence of integrated regional convection and moisture source and transport distances during the EASM 485 season.
We find that the precipitation seasonality modulated by ENSO mainly controls the δ 18 Ow values in the SPR region, with lower (higher) EASM/NSM ratios associated with El Niño (La Niña) phases resulting in higher (lower) δ 18 Ow values. Therefore, we suggest that the interannual variability of speleothem δ 18 O in the SPR region is primarily controlled by precipitation seasonality (i.e., the 490 EASM/NSM ratio) modulated by ENSO. In addition, the ENSO index in the SPR region also significantly correlates with the EASM precipitation amount on interannual timescales and the precipitation δ 18 Ow negatively correlates with the EASM precipitation amount on interannual to decadal timescales, implying that additional studies are needed to disentangle the main driving factor(s) (e.g., EASM precipitation amount vs. EASM/NSM ratio) operating on different timescales. Few speleothem 495 δ 18 O records have been published for the SPR region so far (Jiang et al., 2012;Zhang et al., 2018). Such long-term records, however, are critically needed to examine the climate-proxy relationship both on interannual and on decadal to millennial timescales.

Conclusions
We find that the distribution of seasonal precipitation amount in southeastern China, especially in 500 the SPR region, is different from other regions of the MRC for the time interval of this study (1951. In the SPR region, the mean precipitation amount of the EASM is equivalent to that of the NSM.
However, in northern and southwestern regions of the MRC, the mean percentage of EASM to the annual precipitation amount exceeds 70%. The seasonal δ 18 Op in the MRC shows broadly consistent variations with relatively low and high values for EASM and NSM precipitation, respectively. The low δ 18 Op values 505 20 associated with winter precipitation in northern region of the MRC, however, represent only a minor contribution to δ 18 Ow. Thus, the NSM precipitation in the SPR region also has an important effect on δ 18 Ow, but the δ 18 Ow in northern and southwestern regions is primarily influenced by EASM precipitation.
Based on a statistical analysis of the ENSO index, simulated δ 18 O data and seasonal precipitation amount in the SPR region, we find that less (more) EASM and more (less) NSM precipitation leads to a 510 lower (higher) EASM/NSM ratio resulting in higher (lower) δ 18 Ow in the SPR region during El Niño (La Niña) phases. The AO mainly influences the changes in EASM and annual precipitation amount but not the precipitation seasonality (e.g., EASM/NSM ratio) in the SPR region. Recognizing this spatial difference in seasonal precipitation is essential for a robust interpretation of speleothem δ 18 O in the MRC.
On interannual timescales, speleothem δ 18 O variability in northern and southwestern regions of the MRC 515 is primarily influenced by the EASM or the annual precipitation amount. In the SPR region, however, precipitation seasonality (i.e., the EASM/NSM ratio) modulated by ENSO plays a key role in governing speleothem δ 18 O variability, although integrated regional convection and moisture source and transport distance may also have subordinate impacts. helped to revise the manuscript. G.K and H.Y.L helped to get the IsoGSM simulation data. All authors discussed the results and provided input on the manuscript.

Competing interests
The authors declare no competing interests. 525