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The spring tripole sea surface temperature (SST) anomalies in North Atlantic are an outstanding regional mode of interannual variability. Based on the observed and reanalyzed datasets during 1979–2019, this study reveals the relationship and linking mechanism between the spring tripole North Atlantic SST anomalies and the central China July precipitation (CCJP). Results show that the tripole SST anomalies, especially the warm SST anomalies in the tropical North Atlantic (TNA) and the subpolar North Atlantic (SNA), often cause surplus CCJP through the tropical and extratropical pathways. On the one hand, the spring TNA SST warming induces a pan-tropical climate response with the cooling in the central equatorial Pacific and the warming in the Indo-western Pacific until the following July through a series of air–sea interactions, helping maintain an anomalous anticyclone over the northwest Pacific and transport more warm humid flows to central China. On the other hand, the spring TNA and SNA SST warming persist into the following July and then emanate a wave train extending from the SNA throughout the Eurasian continent to East Asia, which induces an anomalous anticyclone over North China with its southeast flank transporting more cold air to central China. The warm humid flows from the south against the cold air from the north are conductive to the local ascending motion, favoring the increased CCJP. Our results highlight both the tropical and extratropical teleconnection pathways of the North Atlantic SST anomalies affecting the CCJP. This suggests an important seasonal predictor of the regional climate. Significance Statement July is the peak rainy month of central China, with heavy precipitation occurring frequently and often causing serious impacts on the local production and livelihood of millions of people. This study finds that the spring tripole sea surface temperature anomalies in North Atlantic induced by the North Atlantic Oscillation can exert significant impacts on the following July precipitation over central China through both the tropical and extratropical pathways. This improves our understanding of the causes of the surplus July precipitation over central China and has important implications for the seasonal predictability of the regional climate.

During the summer following El Niño, a basin-wide sea surface temperature (SST) warming takes place over the tropical Indian Ocean (TIO), exerting profound influences on the Asian summer monsoon. This is an important source of seasonal predictability for the Asian summer monsoon. Based on observations, however, the present study finds that the relationship between El Niño-Southern Oscillation (ENSO) and the TIO SST anomalies during the decaying summer is asymmetric with a much weaker relationship between La Niña and the TIO SST anomalies relative to El Niño. The analyses show that this asymmetric relationship can be explained by the asymmetries in initial TIO SST, oceanic Rossby wave in the southern Indian Ocean and ENSO decaying rate. In contrast to El Niño events, La Niña events tend to have a stronger initial TIO warming and a less peak intensity with a weaker oceanic Rossby wave response in the southern Indian Ocean. On the other hand, La Niña events tend to decay more slowly with the persistent SST cooling over the central equatorial Pacific in the following summer. The equatorial Pacific SST cooling would induce an anomalous anticyclone via a Gill-type Rossby wave response, weakening the positive feedback between the anomalous cyclone spanning the tropical Northwest Pacific and North Indian oceans and the TIO summer basin-wide SST cooling. These results have important implications for the climate predictability of the Indian Ocean and Asian summer monsoon.

El Niño is a dominant source of interannual climate variability around the world. Based on the observed and reanalyzed datasets for the period of 1958-2019, this study explores the influence of El Niño on the spring precipitation over the Indo-China Peninsula (ICP). The results show that El Niño has a significant negative correlation with the following spring precipitation over the ICP. However, this climatic teleconnection of El Niño was unstable, with an obvious interdecadal strengthening since the early 1990s. During the decaying spring, the El Niño-related sea surface temperature (SST) anomalies would induce an abnormal downward motion along with an anomalous low-level anticyclone over the western North Pacific. Before the early 1990s, such El Niño-induced atmospheric circulation anomalies were located to the east of the ICP, exerting little influence on the spring ICP precipitation. In contrast, since the early 1990s, the abnormal downward motion and anomalous low-level anticyclone extended westwards covering the whole ICP, hampering local spring precipitation. This interdecadal change is owing to a relatively stronger intensity and longer duration of the El Niño-related warm SST anomalies over the tropical central Pacific in the epoch after the early 1990s (1992-2019) than in the previous decades (1958-1991). Our findings highlight a strengthening effect of El Niño on the following spring climate over the ICP since the early 1990s, which has great implications for the regional climate prediction.

Previous studies reveal that the last generation of coupled general circulation models (CGCMs) commonly suffer from the so-called Indian Ocean dipole (IOD)-like biases, lowering the models’ ability in climate prediction and projection. The present study shows that such IOD-like biases are reduced insignificantly or even worsen in CGCMs from phase 5 to phase 6 of the Coupled Model Intercomparison Project (CMIP). The origins of the IOD-like biases in CGCMs are further investigated by comparing model outputs from CMIP and the Atmospheric Model Intercomparison Project (AMIP). The CGCMs’ errors are divided into the biases from the AMIP simulation (AMIP biases) and ocean–atmosphere coupling (coupling biases). For the multimodel ensemble mean, the AMIP (coupling) biases account for about two-thirds (one-third) of the IOD-like CMIP biases. In AMIP simulations, the South Asian summer monsoon (SASM) is overly strong; therefore, it could advect overly large easterly momentum from the south Indian Ocean (IO) to the equator. The resultant equatorial easterly wind bias would initiate the convection–circulation feedback and develop large IOD-like AMIP biases. In contrast, the coupling biases weaken the SASM and hence generate warm SST error over the western IO during boreal summer. Such SST error persists to boreal autumn and triggers the Bjerknes feedback, developing the IOD-like coupling biases. Furthermore, the intermodel spread in the IOD-like CMIP biases is largely explained by the intermodel differences in the coupling biases rather than the AMIP biases. The results imply that substantial efforts should be respectively made on reducing the atmospheric models’ intrinsic monsoon biases as well as advancing the simulations of ocean–atmosphere coupling processes.

The seasonal prediction of precipitation extremes over the Yangtze River basin (YRB) has always been a great challenge. This study investigated the effects of spring soil moisture over the Indo-China Peninsula (ICP) on the following summer mei-yu front and YRB precipitation extremes during 1961–2010. The results indicated that the frequency of summer YRB precipitation extremes was closely associated with the mei-yu front intensity, which exhibited a strong negative correlation with the preceding spring ICP soil moisture. However, the lingering climate influence of the ICP soil moisture was unstable, with an obvious weakening since the early 1990s. Due to its strong memory, an abnormally lower spring soil moisture over the ICP would increase local temperature until the summer by inducing less evapotranspiration. Before the early 1990s, the geopotential height elevation associated with the ICP heating affected the western Pacific subtropical high (WPSH), strengthening the southwesterly summer monsoon. Consequently, the mei-yu front was intensified as more warm, wet air was transported to the YRB, and local precipitation extremes also occurred more frequently associated with abnormal ascending motion mainly maintained by the warm temperature advection. In the early 1990s, the Asian summer monsoon underwent an abrupt shift, with the changing climatological states of the large-scale circulations. Therefore, the similar ICP heating induced by the anomalous soil moisture had different effects on the monsoonal circulation, resulting in weakened responses of the mei-yu front and YRB precipitation extremes since the early 1990s.

The land surface energy exchange over the Indo-China Peninsula (ICP) is important for regulating regional weather and climate. This work investigates the effect of spring soil moisture (SM) over the ICP on the following summer precipitation over the Yangtze River basin (YRB) during 1961–2010. The results show that the spring SM over the ICP has a significant negative correlation with the following summer YRB precipitation. However, this relationship experiences an obvious interdecadal change with a much stronger correlation in the epoch before the early 1990s (1961–91) than in the later decades (1992–2010). In spring, an abnormally lower SM over the ICP could induce less surface evapotranspiration, increasing local temperature until the summer. Before the 1990s, the resultant anomalous ICP heating raises the local geopotential height, resulting in an excessive westward extension of the western Pacific subtropical high (WPSH). Accordingly, the enhanced southwesterly summer monsoon would transport more moisture to the YRB, intensifying the mei-yu front and local precipitation. In the early 1990s, the East Asian summer monsoon underwent an abrupt change with an interdecadal westward extension of the climatic WPSH. Consequently, the similar abnormal ICP surface heating induced by the anomalous SM would have different influences on the monsoonal circulation, causing a much weaker effect on the YRB precipitation in the recent decades.

Whether the state-of-the-art CMIP5 models have different El Niño types and how the degree of modeled El Niño diversity would be impacted by the future global warming are still heavily debated. In this study, cluster analysis is used to investigate El Niño diversity in 30 CMIP5 models. As the method does not rely on any prior knowledge of the patterns of El Niño seen in observations, it provides a practical way to identify the degree of El Niño diversity in models. Under the historical scenario, most models show a poor degree of El Niño diversity in their own model world, primarily due to the lopsided numbers of events belonging to the two modeled El Niño types and the weak compactness of events in each cluster. Four models are found showing significant El Niño diversity, yet none of them captures the longitudinal distributions of the warming centers of the two El Niño types seen in the observations. Heat budget analysis of the sea surface temperature (SST) anomaly suggests that the degree of modeled El Niño diversity is highly related to the climatological zonal SST gradient over the western-central equatorial Pacific in models. As the gradient is weakened in most models under the future high-emission scenario, the degree of modeled El Niño diversity is further reduced in the future. The results indicate that a better simulation of the SST gradient over the western-central equatorial Pacific might allow a more reliable simulation/projection of El Niño diversity in most CMIP5 models.

Climate models consistently project reduced surface warming over the eastern equatorial Indian Ocean (IO) under increased greenhouse gas (GHG) forcing. This IO dipole (IOD)-like warming pattern, regarded as robust based on consistency among models by the new Intergovernmental Panel on Climate Change (IPCC) report, results in a large increase in the frequency of extreme positive IOD (pIOD) events, elevating the risk of climate and weather disasters in the future over IO rim countries. These projections, however, do not consider large model biases in both the mean state and interannual IOD variance. In particular, a “present–future relationship” is identified between the historical simulations and representative concentration pathway (RCP) 8.5 experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble: models with an excessive IOD amplitude bias tend to project a strong IOD-like warming pattern in the mean and a large increase in extreme pIOD occurrences under increased GHG forcing. This relationship links the present simulation errors to future climate projections, and is also consistent with our understanding of Bjerknes ocean–atmosphere feedback. This study calibrates regional climate projections by using this present–future relationship and observed IOD amplitude. The results show that the projected IOD-like pattern of mean changes and frequency increase of extreme pIOD events are largely artifacts of model errors and unlikely to emerge in the future. These results illustrate that a robust projection may still be biased and it is important to consider the model bias effect.

The southern subtropical dipole modes (SSDMs) and southern annular mode (SAM) are important climate modes, which are dominant in the southern middle and high latitudes, respectively, with considerable regional climatic impacts. However, the relationship between the two modes remains unclear. A close inspection reveals that the SAM was significantly correlated with the SSDMs during the austral summer before the mid-1980s. However, the correlations have degraded since then. This decadal shift in the relationship between these two southern dominant modes is due to a weakened connection between the SAM and the subtropical highs that control the SSDMs. This decadal change could be traced back to a poleward shift in the southern westerly belt. El Niño–Southern Oscillation (ENSO) typically plays a moderate role in influencing the precipitation in Australia and a minor role in influencing the precipitation in Africa and South America. Nevertheless, the two southern modes could still affect the austral summer rainfall in the midlatitudes, even though the ENSO signal is absent. All these links between the two southern modes and southern land precipitation may be attributable to the associated transport of moisture in the lower-level circulation.

An excessive cold tongue error in the equatorial Pacific has prevailed in several generations of climate models. However, the causes of this problem remain a mystery, partly owing to uncertainty and/or a lack of observational datasets. Based on the multimodel ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5), this study introduces a novel intermodel approach to identify the bias source by going beyond comparison with observational datasets. Intermodel statistics show that the excessive cold tongue bias could be traced back to a too strong oceanic dynamic cooling linked to a too shallow thermocline along the equatorial Pacific. A heat budget analysis suggests that the excessive oceanic dynamic cooling is balanced by the surface latent heat flux (LHF) adjustment. This is consistent with a variety of oceanic and atmospheric observations but at odds with the popular objectively analyzed air–sea heat fluxes (OAFlux) products. Further analyses suggest an alarming overestimation of OAFlux net surface heat flux (Qnet) into the tropical Pacific, mainly ascribed to observational uncertainly in air specific humidity. Implications for intermodel statistics in assessing model processes, validating observational data, and regulating future climate projections are discussed.

Long-standing biases of climate models limit the skills of climate prediction and projection. Overlooked are tropical Indian Ocean (IO) errors. Based on the phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble, the present study identifies a common error pattern in climate models that resembles the IO dipole (IOD) mode of interannual variability in nature, with a strong equatorial easterly wind bias during boreal autumn accompanied by physically consistent biases in precipitation, sea surface temperature (SST), and subsurface ocean temperature. The analyses show that such IOD-like biases can be traced back to errors in the South Asian summer monsoon. A southwest summer monsoon that is too weak over the Arabian Sea generates a warm SST bias over the western equatorial IO. In boreal autumn, Bjerknes feedback helps amplify the error into an IOD-like bias pattern in wind, precipitation, SST, and subsurface ocean temperature. Such mean state biases result in an interannual IOD variability that is too strong. Most models project an IOD-like future change for the boreal autumn mean state in the global warming scenario, which would result in more frequent occurrences of extreme positive IOD events in the future with important consequences to Indonesia and East Africa. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) characterizes this future IOD-like projection in the mean state as robust based on consistency among models, but the authors’ results cast doubts on this conclusion since models with larger IOD amplitude biases tend to produce stronger IOD-like projected changes in the future.

Errors of coupled general circulation models (CGCMs) limit their utility for climate prediction and projection. Origins of and feedback for tropical biases are investigated in the historical climate simulations of 18 CGCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), together with the available Atmospheric Model Intercomparison Project (AMIP) simulations. Based on an intermodel empirical orthogonal function (EOF) analysis of tropical Pacific precipitation, the excessive equatorial Pacific cold tongue and double intertropical convergence zone (ITCZ) stand out as the most prominent errors of the current generation of CGCMs. The comparison of CMIP–AMIP pairs enables us to identify whether a given type of errors originates from atmospheric models. The equatorial Pacific cold tongue bias is associated with deficient precipitation and surface easterly wind biases in the western half of the basin in CGCMs, but these errors are absent in atmosphere-only models, indicating that the errors arise from the interaction with the ocean via Bjerknes feedback. For the double ITCZ problem, excessive precipitation south of the equator correlates well with excessive downward solar radiation in the Southern Hemisphere (SH) midlatitudes, an error traced back to atmospheric model simulations of cloud during austral spring and summer. This extratropical forcing of the ITCZ displacements is mediated by tropical ocean–atmosphere interaction and is consistent with recent studies of ocean–atmospheric energy transport balance.

Given the complexity of trends in the actual climate system, distinguishing between different trends and different trend modes is important for climate research. This study introduces a new method called “trend singular value decomposition (TSVD) analysis,” which is designed for systematically extracting coupled trend modes, albeit small, by performing an eigenanalysis of the inverse-rank covariance matrix between two fields. Applications to simple time series models and annual mean surface latent heat flux (LHF) and SST data for 1958–2006 are presented and discussed. Results show that the TSVD analysis can capture different coherent trends into different leading modes. The first TSVD mode between the global LHF and SST anomalies, similar to the first conventional SVD mode, generally represents a large-scale increasing LHF trend induced by a warming SST trend; whereas, interestingly, unlike the second SVD mode that is mainly associated with the familiar ENSO, the second TSVD mode is mainly associated with the Pacific decadal oscillation (PDO). TSVD analysis casts the (global) long-term and (Pacific) decadal trends into the leading two modes, respectively. Compared to SVD analysis, the advantages of the TSVD analysis in detecting coupled low-frequency modes are even more evident in the tropical Pacific (TP), where the coherent trend signals (i.e., the long-term trends and the decadal trends) are smaller than the ENSO-related signals. Thus, TSVD analysis performs better than SVD analysis when focusing on trends rather than on maximum covariance patterns, particularly on relatively small coherent trend patterns, such as the coupled long-term trends and decadal trends in the TP.