1 Introduction

East Asian Summer Monsoon (EASM) rainfall is crucial for one of the most densely populated regions in the world. The population of China strongly depends on EASM rainfall for socio-economic development, such as agriculture, industry, transportation, and infrastructure systems. The EASM is subject to interannual variability that induces floods and droughts and can cause casualties and economic losses (Song and Zhou 2014; Jiang et al. 2021). Therefore, it is necessary to develop weather and climate models that can accurately predict variability and projected change in summer monsoon rainfall over China. Despite recent successes in seasonal predictions for specific regions in China [e.g., the Yangtze River Valley forecast from the Climate Science for Service Partnership China; Li et al. (2016)], the simulation of summer monsoon rainfall in global numerical models is still challenging. Models often overestimate rainfall in southern China and underestimate rainfall in northern China. For example, in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) atmospheric general circulation models (AGCMs), these rainfall biases are often associated with a weak and southeastward shifted western North Pacific subtropical high (WNPSH), which is linked to a southward shift in moisture transports to East Asia (EA; Song and Zhou 2014; He and Zhou 2014). Also, coupled general circulation models (CGCMs) underestimate the southeast-northwest precipitation gradient, show a deficient strength of the EASM circulation, and overestimate the spatial and magnitude of interannual precipitation variability in EA (Jiang et al. 2016). The Met Office Unified Model (MetUM) produces too much rainfall in southern China and too little rainfall in northern China [e.g., MetUM Global Atmosphere 6.0; Stephan et al. (2018)]. Guo et al. (2020) identified systematic errors of MetUM atmosphere-only and atmosphere-ocean coupled configurations in simulating the moisture sources of East Asian precipitation. Their results suggest that MetUM precipitation biases in EA are linked to underestimated moisture contributions from the tropical Indian Ocean and overestimated contributions from the Eurasian continent.

Other hypothesised sources of EASM systematic model errors include biases in the large-scale meridional tropospheric temperature gradient (Dai et al. 2013) and coarsely resolved topography over EA (Wu et al. 2017). The latter is associated with an underestimated ascent over the mountains of subtropical EA and the Tibetan Plateau. This creates overestimated southerly winds and, compared with observations and high-resolution models, reduces East Asian pre-summer precipitation further north. Another source of hypothesised systematic errors in the thermal forcing is biasing in Pacific and Indian Ocean SSTs. The latter is associated with the representation of the Walker circulation and wave propagation from the Indian to the Pacific Oceans (Wang et al. 2015; Levine et al. 2021). Although not statistically significant, ERSSTv5 and both GOML3 regressions also show differences in the SST magnitude in the Indian Ocean. While ERSSTv5 reaches up to 0.3 °C in a large portion of the Arabian Sea and the equatorial Indian Ocean, SSTs in both GOML3 simulations reach up to 0.05 °C.

Fig. 8
figure 8

Mean JJAS SSTs (shaded, \(^\circ \text {C}\)) and winds at 850 hPa (arrows, m\(\, {\text {s}^{-1}}\)) regressed onto standardised JJAS Niño 3.4 SSTs for a ERA5 and ERSSTv5, b GOML3-OBS_ENSO, and c GOML3-GC3_ENSO. Stippling indicates SSTs significant at the 5%. Wind vectors are drawn in navy blue color when at least one of the components of the vector is significant at the 5% level using the Student’s t-test. The green box delimits the Niño 3.4 region

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During JJAS, ERA5 and GOML3 regression analyses show that western Pacific anomalous equatorial westerly winds at 850 hPa positively correlated with ENSO. However, the anomalous westerlies over the West Pacific extend northwards 15° N in both GOML3 experiments (Fig. 9b, c) during JJAS. In both GOML3 experiments, the anomalous westerly winds weaken the WNPSH, centred at \(\approx {25}^\circ\) N, \(175^\circ\) E. These cyclonic circulation anomalies extend to continental China. The 850 hPa anomalous circulation is associated with reduced rainfall in South East China in GOML3 experiments. Also, at low levels in the troposphere, both GOML3-OBS_ENSO and GOML3-GC3_ENSO simulations show an anomalous anticyclonic circulation over India and anomalous northeasterlies associated with a weakened Indian monsoon circulation over the northern Indian Ocean in El Niño years (Fig. 9b, c, respectively). These anomalous regional circulations are associated with errors in the signal of anomalous precipitation simulated over India, the Bay of Bengal, and the Arabian Sea located west of India with GOML3.

Fig. 9
figure 9

Mean JJAS rainfall (shaded, mm\(\, \text {day}^{-1}\)) and winds at 850 hPa (arrows, m\(\, \text {s}^{-1}\)) regressed onto standardised JJAS Niño 3.4 SSTs for a ERA5 and ERSSTv5, b GOML3-OBS_ENSO, and c GOML3-GC3_ENSO experiments. Stippling indicates rainfall significance at the 5% level using the Student’s t-test. Wind vectors are drawn in black color when at least one of the components of the vector is significant at the 5% level using the Student’s t-test

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JJAS ERA5 regressed winds onto Nino 3.4 suggest that the entire North Pacific Subtropical High (NPSH, centred at \(\approx {35}^\circ\) N, \(177^\circ\) E) is weakened during develo** El Niño (Fig. 9a). However, the anomalous circulation associated with the NPSH is weaker in GOML3-OBS_ENSO during develo** ENSO, and it is centred about 7 degrees further east in GOML3-GC3_ENSO regressions (\(\approx {35}^\circ\) N, 170\(^\circ\) W).

To understand the overall effect of GC3 mean-state errors influencing the ENSO teleconnection to the EASM rainfall, we analyse the GOML3-OBS_ENSO and GOML3-GC3_ENSO regressions. JJAS precipitation anomalies over \(\approx {{20^\circ -40^\circ }}\) N, simulated with GOML3-OBS_ENSO (Fig. 9b) and GOML3-GC3_ENSO (Fig. 9c), are significantly anticorrelated with Niño 3.4, with up to \(\approx {-0.5}\) and \(\approx {-1}\) mm \({\text {day}^{-1}}\), respectively. However, JJAS ERA5 rainfall is significantly anticorrelated with simultaneous SSTs from the Niño 3.4 region (Fig. 9a), only over the northernmost portion of South East China (\(\approx {30}^\circ -40^\circ\) N). This regression analysis suggests that during develo** El Niño (La Niña) episodes, this area experiences drier (wetter) conditions.

The major biases in the ENSO teleconnection to the EASM rainfall with GC3 are identified when directly comparing GOML3-GC3_ENSO against GOML3-OBS_ENSO rainfall simulations. At the beginning of the EASM season, GOML3-GC3_ENSO presents dry conditions in southeast China, the Bay of Bengal, and the Indochinese Peninsula, during both develo** El Niño and La Niña (not shown). Additionally, GOML3-GC3_ENSO presents wet conditions in the Maritime continent. The aforementioned anomalous conditions resemble the ones present in the GOML3-GC3gbl experiment in June (Fig. 3a), implying that these ENSO-teleconnection biases between GOML3-GC3_ENSO simulation are predominately related to GC3 SST biases. In July, GOML3-GC3_ENSO simulations are drier for a large area of southeast China (between \(\approx {30}^\circ -40^\circ\) N) during develo** El Niño events (not shown). However, during develo** La Niña, GC3 presents more rainfall in most of southeast China. At this point of the simulations, the EASM rainfall does not penetrate northwards enough, implying that GOML3-GC3_ENSO exacerbates the dry conditions during the EASM in northern southeast China during both ENSO phases.

In August, GOML3-GC3_ENSO enhances the Indian Monsoon circulation in both develo** ENSO phases, leading to more rainfall in continental India and the Bay of Bengal and less rainfall over the equatorial Indian Ocean (not shown), similar to the ones in the GOML3-GC3gbl experiment in August (Fig. 3c).

Most of the differences in JJAS precipitation between ERA5 and both GOML3 simulations are seen over southernmost South East Asia (\(\approx {20}^\circ -30^\circ\) N): while ERA5 rainfall is positively correlated with Niño 3.4 SSTs in southernmost East China and the Indochinese Peninsula (Fig. 9a), GOML3-OBS_ENSO and GOML3-GC3_ENSO are significantly anticorrelated over the region mentioned above (Fig. 9b, c, respectively). These analyses suggest that GOML3 presents a systematic bias of underestimated rainfall in South East China during El Niño years. The GC3 ocean mean state bias exacerbates this error, which affects the ENSO teleconnection to the EASM by simulating drier (wetter) conditions over South East China during El Niño (La Niña) events.

ERA5 and both GOML3 experiments differ in the location of anomalous rainfall along the central Pacific Ocean. While both GOML3 experiments show a significant positive correlation of rainfall with ENSO, ERA5 shows a negative correlation that extends \(\approx {7-10}^\circ\) N latitude. This particular feature in ERA5-regressed JJAS rainfall is related to the anomalous equatorward (poleward) shift of the Intertropical Convergence Zone (ITCZ) during El Niño (La Niña) events (e.g., Berry and Reeder 2014), which is not properly captured in either GOML3 experiment.

We investigate the errors in ENSO teleconnection pathways to the EASM and compute the moisture sources for JJAS rainfall in the south China box (defined previously in Sect. 2.2). Figure 10 shows the regressions of moisture sources and moisture transport onto Niño 3.4 SSTs. These regression analyses suggest that ERA5 rainfall in the south China box is positively correlated with moisture sources from continental India, the Arabian Sea, the Bay of Bengal, the south Maritime Continent, and the region of Guizhou and Guangxi in South East China during El Niño. Simultaneously, anomalous northeastwards moisture transport from the Tibetan Plateau and continental India is positively correlated with anomalies of rainfall in the South China box.

Fig. 10
figure 10

JJAS tracked moisture sources of rainfall (shaded, mm\(\, \text {day}^{-1}\)) and tracked vertically integrated moisture transport (arrows, \(\text {m}^{3}\, \text {month}^{-1}\)) for the South China box (green box) obtained using the WAM-2layers, regressed onto standardised JJAS Niño 3.4 SSTs for a ERA5, b GOML3-OBS_ENSO, and c GOML3-GC3_ENSO. Stippling indicates moisture sources significant at the 5% level using the Student’s t-test. Vertically integrated moisture transport vectors are drawn in black color when at least one of the components of the vector is significant at the 5% level using the Student’s t-test

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GOML3 errors in simulating the moisture sources of rainfall in the south China box are found mainly in the Indian Ocean (Fig. 10), having the opposite sign to the ERA5 regressions. The GOML3 simulations also present a northward bias in the location of moisture transport vectors over the Pacific Ocean. As discussed, the precipitation in the south China box during JJAS is weakened (enhanced) by MetUM GOML3 experiments during El Niño (La Niña). The regression analysis for GOML3 experiments suggests that, during El Niño events, the reduced rainfall in the south China box simulated with GOML3-OBS_ENSO and GOML3-GC3_ENSO is associated with reduced moisture from a large area of the equatorial Indian Ocean, the Bay of Bengal, the Arabian Sea and the Maritime Continent. These regressions also suggest that during El Niño, the reduced rainfall simulated with both GOML3 experiments in the south China box is associated with a weaker moisture transport from the Indian monsoon circulation. In all cases, both GOML3 simulations show opposite conditions during La Niña. The biases with GOML3 precipitation in the south China box come from errors capturing important sources of moisture and moisture transport from the Indian Ocean and the Maritime Continent.

3.4.2 IOD

To analyse errors in the IOD teleconnection pathways to the EASM, we linearly regress GOML3-OBS_IOD and GOML3-GC3_IOD simulations of seasonal meteorological variables onto the standardised seasonal IOD index. In this study, the IOD index is calculated as the difference between SST anomalies of two regions of the tropical Indian Ocean, named IOD west (\(50^\circ\) E–\(70^\circ\) E longitude, \(10^\circ\) S–\(10^\circ\) N latitude) and IOD east (\(90^\circ\) E–\(110^\circ\) E longitude, \(10^\circ\) S–\(0^\circ\) S latitude) (Saji et al. 1999). Similarly, we calculate observed linear regressions of seasonal SSTs of ERSSTv5 and ERA5 meteorological variables onto standardised seasonal IOD index calculated with ERSSTv5 data.

JJAS ERSSTv5 SSTs regressed onto the observed IOD index show a typical pattern of the positive IOD (Fig. 11a). In addition, ERA5 anomalous southeasterlies at 850 hPa, which extend from the Tasman Sea to the equatorial Indian Ocean (to \(\approx {60}^\circ\) E), are positively correlated with the IOD index [as in Saji et al. (1999)]. Simultaneously, this regression analysis also shows significant anomalous low-level, westerly winds over continental India and the Bay of Bengal, extending east to reach the equatorial Pacific Ocean.

Fig. 11
figure 11

Mean JJAS SSTs (shaded, \(^\circ \text {C}\)) and winds at 850 hPa (arrows, m\(\, \text {s}^{-1}\)) regressed onto standardised JJAS IOD index for a ERA5 and ERSSTv5, b GOML3-OBS_IOD, and c GOML3-GC3_IOD. Stippling indicates SSTs significant at the 5% level using the Student’s t-test. Wind vectors are drawn in navy blue color when at least one of the components of the vector is significant at the 5% level using the Student’s t-test

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GOML3-OBS_IOD SSTs and 850 hPa wind regression analysis show southeasterlies over the equatorial Indian Ocean and the Bay of Bengal that are positively correlated with the IOD index (Fig. 11b). This significant circulation continues towards the Bay of Bengal, where anomalous westerlies extend from the Indochinese peninsula to the Pacific Ocean (at \(\approx {15}^\circ\) N latitude).

Differences between ERSSTv5 and GOML3-OBS_IOD-regressed SSTs are found in the Tasman Sea, the east side of the Maritime Continent, and the subtropical North Pacific. GOML3-OBS_IOD circulation biases are found in the Pacific Ocean, where anomalous 850 hPa easterlies in positive IOD years are further north than in ERA5. This change in the circulation influences the southward flank of an anomalous weakened WNPSH centred over the Philippine Sea. The GOML3-GC3_IOD 850 hPa easterlies over the South China Sea and the Indochinese peninsula oppose the direction of ERA5-regressed winds.

JJAS ERA5 rainfall in southeast China is positively correlated with the IOD index with regressions up to 0.5 mm \(\text {day}^{-1}\) and negatively correlated with south Indian and Australian rainfall (Fig. 12a). Regressed ERA5 rainfall presents a dipole of negative anomalies in the IOD east region with up to 1.5 mm \(\text {day}^{-1}\) and positive anomalies in the IOD west part of up to 2 mm \(\text {day}^{-1}\).

Fig. 12
figure 12

Mean JJAS precipitation (shaded, \(\text {mm}\, \text {day}^{-1}\)) and winds at 850 hPa (arrows, m\(\, \text {s}^{-1}\)) regressed onto standardised JJAS IOD index for a ERA5 and ERSSTv5, b GOML3-OBS_IOD, and c GOML3-GC3_IOD. Stippling indicates indicates precipitation significant at the 5% level using the Student’s t-test. Wind vectors are drawn in navy color when at least one of the components of the vector is significant at the 5% level using the Student’s t-test

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GOML3-OBS_IOD underestimates the magnitude of anomalous rainfall in both IOD west and IOD east regions, with about 1 mm \(\text {day}^{-1}\) biases in both cases. For positive IOD, GOML3-OBS_IOD shows less rainfall than ERA5 over most South East China, the East China Sea, and Bangladesh and more precipitation in Australia (Fig. 12b).

On the other hand, GOML3-GC3_IOD shows more rainfall in positive IOD than GOML3-OBS_IOD in southeastern China, Bangladesh, Myanmar, and Australia (Fig. 12c). This analysis suggests that GOML3-GC3_IOD presents positive changes in the regression slope against GOML3-OBS_IOD during positive IOD events. As a result, GOML3-GC3_IOD offers a better estimation of the sign of anomalous rainfall over southeastern China. Different from the negative rainfall biases in GOML3-GC3gbl (Fig. 6d) during JJAS, GOML3-GC3_IOD regressed slope shows a positive difference in rainfall over southeastern China. This analysis suggests that during IOD episodes, GOML3_GC3 represents the correct sign of rainfall anomalies over EASM rainfall over most of southeast China.

To investigate the errors in the IOD teleconnection pathways to the EASM, we compute the moisture sources for JJAS rainfall for the South China box. First, we determine the moisture sources for precipitation over this box in ERA5 and GOML3 experiments using the WAM-2layers back trajectory tool (Fig. 13).

Fig. 13
figure 13

JJAS tracked moisture sources of rainfall (shaded, mm\(\, \text {day}^{-1}\)) and tracked vertically integrated moisture transport (arrows, \(\text {m}^{3}\, \text {month}^{-1}\)) for the South China box (green box) obtained using the WAM-2layers, regressed onto standardised JJAS IOD index for a ERA5, b GOML3-OBS_IOD, and c GOML3-GC3_IOD. Stippling indicates moisture sources significant at the 5% level. Vertically integrated moisture transport vectors are drawn in black color when at least one of the components of the vector is significant at the 5% level

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ERA5 regressed moisture sources show that in IOD positive years, increased evaporation from the IOD west region, the Bay of Bengal, and the Arabian Sea, precipitates over the south China box (Fig. 13a). Increased moisture transport from these regions is related to increased rainfall over the South China box.

Regression analysis shows that GOML3-OBS_IOD underestimates moisture sources over continental China and the east IOD region, which contributes to less rainfall in the south China box with GOML3-OBS_IOD (Fig. 13b). GOML3-OBS_IOD experiments also have a strong positive moisture bias that extends from Laos to the Philippines, larger than 1 mm \(\text {month}^{-1}\). Regressed moisture transports from the Philippine Sea are weaker than that in ERA5.

GOML3-OBS_IOD underestimate moisture sources from the South China Sea, IOD east, the Bay of Bengal, the Arabic Sea, and India (Fig. 13c). However, GC3 SST biases enhance the EASM flow. Therefore, GC3 corrects the underestimation of moisture sources in east IOD and southeast China from GOML3-OBS_IOD, and the moisture transport in the Indian Ocean is better represented with GC3.

4 Discussion and summary

In this study, we analyse simulations from the MetUM-GOML3, coupled model of the MetUM GA7 atmosphere to a mixed-layer ocean, which constrains the SSTs to observations (GOML3-OBS) or GC3 SSTs. Furthermore, the GOML3-OBS experiment is used as a reference to analyse different coupled climate-length MetUM-GC3 runs.

GOML experiments with global GC3 SST biases allow us to establish the effects of regional SST errors on the EASM, in which GC3 global SST biases cause a substantial shift in the seasonal cycle of the EASM: a late northward progression of the monsoon in June, and an early retreat of the EASM (early August) reduces rainfall over most of northern China.

Another set of GOML experiments with individual regional SST biases in the Atlantic, Pacific, and Indian Oceans allow us to establish the effects of basin-scale SST errors on the EASM. This approach helps test the remote influence of the three different ocean basins on the EASM.

It is found that the simulated rainfall over East Asia is more sensitive to SST errors in the Indian and Pacific oceans than those in the Atlantic Ocean. However, these analyses show the cancellation of effects between Pacific Ocean biases and Atlantic and Indian Ocean biases. GC3 SST biases in the Indian and Atlantic Oceans reduce JJAS rainfall over most southeastern China. In both cases, these conditions are related to a later northward EASM progression and an earlier monsoon retreat during September. Conversely, GC3 Pacific SST biases increase rainfall over southeastern China, associated with a systematic increase of moisture convergence in the equatorial West Pacific and southeast China and moisture divergence in the Philippine and South China Sea.

An essential finding of the GOML experiments is the opposite effect of the Pacific and the Atlantic Ocean SST biases on the North Pacific circulation. An increase (decrease) in EASM rainfall is linked to an enhanced (weakened) North Pacific subtropical high circulation. In both cases, changes in the circulation in the West Pacific, near the Philippines, play a key role in the biases of local evaporation and moisture transport towards South China.

Our study shows that MetUM-GOML3 presents similar cold SST biases in the Indian Ocean as its predecessors (Levine and Turner 2012; Levine et al. 2021). The low-level winds and rainfall associated with these SST biases characterise an enhanced Indian summer Monsoon. Additionally, our WAM-2layers analysis shows that evaporation and moisture transport biases in the Indian Ocean result in more extensive EASM changes in rainfall. This coincides with multiple studies that found that the Indian Ocean provides the largest amount of moisture during boreal summer for rainfall in southeastern EA (Guo et al. 2019; Sun et al. 2015; Baker et al. 2015).

This study also determined how GC3 SST biases affect the ENSO and the IOD teleconnection to the EASM. The results indicate that ENSO-associated rainfall anomalies over South China are not correctly simulated with the GOML model, even with observed climatological SSTs (Fig. 9).

GOML3 experiments do not accurately represent the equatorward anomalous rainfall migration during the warm episodes of ENSO, which is associated with an anomalous equatorward migration of the ITCZ. In addition, among the GOML3 biases during ENSO, we found anomalous lower troposphere westerly winds located further northwards along the tropical Pacific. Therefore, MetUM-GOML3 experiments have an incorrect sign of the anomalous moisture sources related to the anomalous seasonal rainfall over South China, simulating reduced moisture sources of rainfall in the Bay of Bengal and the Arabian Sea. GC3 SSTs biases worsen the bias of ENSO-associated precipitation over South China, being related to amplified biases of an anomalous moisture source, mainly in the Philippine Sea, the Indian Ocean, and the Tibetan Plateau.

Finally, the effect of GC3 SST biases on the simulated IOD teleconnection to the EASM is studied. The circulation biases are widely found in the tropical Pacific Ocean, where anomalous low-level winds are located further north. This is associated with biases in the intensity and location of the WNPSH and its variability, which strongly influences the location and strength of the GOML3 representation of the EASM rainfall.