In order to determine the primary temporal variations of the May rainfall anomalies over the MLYZR, the MLYZR index (MLYZRI) was constructed by averaging the May rainfall anomaly data over the MLYZR. Heavy rainfall years are defined by those years when the MLYZRI was larger than the STD for 1983–2018. To ensure the consistency of the spatial distributions of rainfall anomalies in the heavy rainfall years, we continued by using the method described above, that is, only those grids in which all of the single heavy rainfall years have the same sign as the MME results are marked with black dots. This method is also used in the following figures. The composite result of the observed May rainfall anomalies (Fig. 4a), shows that there are significant positive rainfall anomalies over southeastern China, but there are obvious negative rainfall anomalies over the South China Sea (SCS) and over the oceans to the east of the Philippines. Compared to the observed heavy rainfall over the MLYZR in May, the simultaneous composite MME rainfall with a 1-month lead is much weaker, indicating a somewhat limited forecasting skill. Whereas for the MME rainfall anomalies with a 3-month lead, the simultaneous composite results are insignificant for all of mainland China, with no forecasting skill. Figures 4d and 4e show the composite MME rainfall anomalies related to the corresponding MME heavy rainfall over the MLYZR. The pattern of the rainfall anomalies shown in Figs. 4d and 4e resembles the observations. Therefore, the models have the ability to forecast the rainfall pattern shown in Fig. 4a, but they are out-of-phase with the observations. The inconsistent spatial distributions of the rainfall anomalies shown above are consistent with the relatively low TCC between the MME and the observations, especially for the MME with a 3-month lead.
As was discussed in the introduction, tropical SSTA are generally considered to be the major sources of seasonal predictability. Therefore, the SSTAs that occurred concurrently with the heavy rainfall over the MLYZR (represented by those years for which the MLYZRI is larger than the STD) were diagnosed in the observations and models. The positive SSTAs concurrent with heavy rainfall over the MLYZR particularly occur over the tropical eastern Indian Ocean (EIO), the South China Sea, and its adjacent regions (Fig. 5a). In addition, significant positive SSTAs also occur in limited areas of the subtropical central Pacific Ocean, indicating the insignificant effect of ENSO on the contemporaneous heavy rainfall over the MLYZR. The predicted SSTAs with a 1-month lead can reproduce the pattern of the observed SSTAs well, as is shown in Fig. 5a. However, for the predicted SSTAs with a 3-month lead, the warm SSTAs that were originally located in the eastern tropical Indian Ocean extend into the southeastern and southwestern Indian Ocean. The heavy MME rainfall over the MLYZR is strongly related to ENSO in the models, which is demonstrated by the strong positive SSTAs over the tropical eastern Pacific Ocean for both the 1-month and 3-month leads (Figs. 5d and 5e). The years of heavy rainfall over the MLYZR based on observation and model forecast are listed in Table 1, which the results show some differences between observation and models. We also remark the El Nino years with asterisk, as shown, there are two El Nino years when it occurred heavy rainfall over the MLYZR in the observation, but three for the model forecasts with 1-month lead, and even five for the model forecasts with 3-month lead. The inconsistency between observation and model forecasts also indicate that the occurrence of heavy precipitation in the models is likely to be mistakenly modulated by El Niño, and such modulation effect may be amplified with the increase of the leading month.
Correlation analysis is carried out between the rainfall over China and the Niño3 index for both the observations and the model results in order to explore their relationships. The observed rainfall over China, including the MLYZR, has no significant contemporaneous correlation with the observed Niño3 index (Fig. 6a), which is consistent with the spatial distribution of the SSTAs shown in Fig. 5a. However, for the models, the correlations and the consistencies between the predicted rainfall anomalies over southeastern China produced by the four individual models and the contemporaneous Niño3 index are significant for both the 1-month lead and the 3-month lead (Figs. 6b, c). The lead correlation reveals that the significant correlation between the observed rainfall over the MLYZR and observed Niño3 index slightly exceed the 95% confidence level in the preceding December and current February, indicating the lagged response of the atmospheric circulation to the El Niño signal. In contrast, the MME rainfall over the MLYZR is significantly correlated with the MME Niño3 index for both the 1-month and 3-month leads. The forecasts of the MME and the individual models all show this lagging strong influence of ENSO on the rainfall over the MLYZR, and the results with a 3-month lead have a higher correlation coefficient than those with a 1-month lead (Figs. 6d, e). In short, based on the observations, ENSO is not an essential factor causing the heavy rainfall over the MLYZR. However, the models all overestimate the relationship between ENSO and the heavy rainfall over the MLYZR. Wu et al. (2000) have long since concluded that the correlation between the SSTAs in the equatorial central-eastern Pacific and China’s climate is only skin deep. In fact, the Indian Ocean SSTA is the real reason for the direct causal relationship with China’s climate.
Considering the importance of the SSTA in the EIO, we calculated the area-averaged SSTA over the EIO region (15ºS–15ºN, 80ºE–110ºE; Fig. 7a) and defined it as the EIO index. The relationship between the EIO index and the rainfall over China were also explored. The observed May EIO index is significantly correlated with the observed May rainfall anomalies in the MLYZR. The models all capture the relationship between the EIO index and the rainfall anomalies over the MLYZR well. The lead correlation shows that the maximum correlation coefficient occurs in May for both the observations and models, verifying the direct influence of the EIO SSTA on the rainfall anomalies over the MLYZR.
Since we found a strong relationship between the SSTA in the EIO and the rainfall anomalies over the MLYZR in May, it was necessary to investigate the spatial-temporal evolution of the SSTA in the EIO. During the years in which there was heavy rainfall over the MLYZR, positive SSTAs were observed over the central tropical Indian Ocean from December (-1) to March (0) (Figs. 8a–d). Here, (-1) and (0) indicate the preceding and current year of the heavy rainfall over the MLYZR, respectively. The positive SSTA migrates to the northeast in April, resulting in the warming of the eastern part of the Bay of Bengal (Fig. 8e). In May, the positive SSTA keeps moving toward the South China Sea and the adjacent seas. Meanwhile, anomalous south-westerlies occur in the left edge of the low-level anti-cyclonic anomalies located around the northwestern Pacific, which transport more moisture from the South China Sea to the MLYZR, leading to the heavy rainfall over the MLYZR. This process is consistent with previous findings of the influence of the Indian Ocean SSTA on the summer rainfall over the MLYZR (Du et al. 2009; Xie et al. 2009). The SSTA transition from April to May is most noteworthy, and it is of greatest importance to the occurrence of the heavy rainfall in the MLYZR.
We further studied the SSTA transition from April to May in the models in order to explore the possible factors that affect the heavy rainfall over the MLYZR. During the transition period from April to May in the years in which heavy rainfall was observed over the MLYZR, the MME can forecast the warming process from the eastern Bay of Bengal to the southern part of the South China Sea with a 1-month lead (Figs. 9a, b), but it failed for a 3-month lead (Figs. 9c, d). Correspondingly, there is an anti-cyclonic anomaly at 850 hPa in May for a 1-month lead, but the low-level 850 hPa wind anomalies are disorganized for a 3-month lead. For the transition period from April to May for the MME for the years with heavy rainfall over the MLYZR, the MME SSTA exhibits obvious warming features for both 1-month and 3-month leads, and furthermore, almost the entire tropical Indian Ocean is robust, showing basin-wide warming (Figs. 9e–h). Similarly, there are also low-level anti-cyclonic anomalies located around the northwestern Pacific, which correspond well with the heavy rainfall over the MLYZR (Fig. 4d, e).
The climate models have the highest forecasting abilities for the SSTAs in the tropical Oceans. The correlation coefficients between the observations and the forecasts of the Niño3 index and the EIO index all exceed 0.7 for every individual model, and the MME results exceed 0.8, indicating high levels of prediction skill. Thus, the forecasting of the monthly SSTA is not the main reason that the models can basically obtain the rainfall anomalies in the MLYZR with a 1-month lead but fail with a 3-month lead. As a matter of fact, what really matters is whether the model can forecast the SSTA processes from April to May. We selected the SSTA differences between May and April in the tropical Indian Ocean, the SCS, and its adjacent seas and calculated its correlation coefficients with the May rainfall anomalies in the MLYZR. The observed results show that the areas significantly related to the rainfall anomalies in the MLYZR in May are mainly located in the northeastern SCS and the eastern part of Taiwan Island (Fig. 10a). In comparison, in the models, the regions that are significantly related to the models’ rainfall anomalies in the MLYZR are located in the northeastern Indian Ocean and in the southern SCS (Figs. 10b, c). For a 1-month lead, from May to April, the forecasted SSTA differences between the models’ MLYZR rainfall related regions (Northeastern Indian Ocean, NEIO, 0–18ºN, 80–120ºE) and the observed key regions (Northeastern SCS, NSCS, 10–25ºN, 110–125ºE) have a high synchronous correlation coefficient, with 0.52 for the MME forecasts with a 1-month lead and 0.32–0.52 for the four individual models. However, for the forecasts with a 3-month lead, the correlation coefficient for the MME is only 0.1, and those for the four individual model are − 0.08–0.24. This significant inconsistency indicated by the correlation coefficients mentioned above satisfactorily explains why the models can generally forecast the heavy rainfalls over the MLYZR with a 1-month lead but fail for a 3-month lead. In addition, the relationship between the SSTA differences from April to May over the NEIO and the Niño3 index of the models was studied. As is shown in Fig. 10d, the MME results with a 1-month lead have a correlation coefficient of almost 0.6, and those for the four individual models are 0.41–0.68. While for the MME results with a 3-month lead, the correlation coefficient is 0.68, and those for the four individual models are 0.46–0.6. This indicates that the El Niño events may trigger warming, especially over the NEIO, from April to May, thus inducing the heavy rainfall over the MLYZR in the models. It should also be noted that the observed SSTA differences from April to May over the NSCS have no significant correlation with the observed Niño3 index, i.e., the correlation coefficient is only 0.1. These results indicate that in the real world, the warming processes over the NSCS, which are vital to the occurrence of heavy rainfall over the MLYZR, are not the direct results of El Niño events, and this phenomenon should be studied more in-depth in the future.