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July is the rainy peak month of central China, with a large interannual variation of local precipitation often causing serious droughts and floods. The seasonal prediction of the central China July precipitation (CCJP) is an important but still challenging task. Here, we suggest several robust seasonal predictors for the CCJP, including the preceding winter intensity of El Niño‐South Oscillation (ENSO), the winter‐to‐spring decaying rate of ENSO signals in the central Pacific, as well as the spring tropical and subpolar North Atlantic sea surface temperature anomalies. A physics‐based empirical model is then developed to predict the CCJP by using the principal component regression of the aforementioned seasonal predictors. In our statistical model, the seasonal prediction skill of the CCJP is high, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This suggests a skillful seasonal prediction of the CCJP, with potentially enormous benefits for the local society and economy. Plain Language Summary: July contributes about 20% of annual precipitation for the densely populated central China, which could exert tremendous socio‐economic impacts over the region, including agriculture, water resources, food security, ecosystems, disaster mitigation, infrastructure construction, human health, and so on. Thus, how to skillfully predict the interannual variation of the central China July precipitation (CCJP) is a widespread scientific and socio‐economic concern. This work establishes a statistical model for the seasonal prediction of the CCJP by combining the physical precursor factors that drive the interannual variation of the CCJP. Our physics‐based empirical model can well predict the CCJP at one‐season lead, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This provides a substantial skill of the seasonal prediction of the CCJP and is of potentially great importance to the regional agrarian‐based livelihood of hundreds of millions of people. Key Points: The Pacific and North Atlantic sea surface temperature precursors are suggested as robust seasonal predictors of the central China July precipitation (CCJP)A physics‐based empirical prediction model of the CCJP is developed based on the principal component regression of the seasonal predictorsThe prediction skill of the CCJP in our statistical model is high, with a cross‐validated reforecast skill at 0.81 during 1993–2021 [ABSTRACT FROM AUTHOR]

Eastern China (EC) suffered an extreme drought with long-lasting duration and record-breaking intensity in late summer–autumn 2019. Our diagnosed results show that the central Pacific (CP) El Niño, in tandem with warm sea surface temperature anomalies (SSTAs) over the Kuroshio Extension (KE) region, induces the meridionally elongated cyclonic circulation anomalies stretching from the western North Pacific (WNP) to the Yellow Sea. Its western flank corresponds to overwhelming low-level northerly wind anomalies over EC, which result in deficient moisture and anomalous descent over EC and hence cause the extreme drought in 2019. To investigate the relative contributions of SSTAs over different regions, we performed sensitivity experiments, and analyzed the relationship between extreme drought like what occurred in 2019 (a 2019Drought-like event) and the SSTAs in CMIP6 historical simulations. Modeling evidences reveal that both warm SSTAs over the central equatorial Pacific and the KE region are indispensable for shaping the meridionally elongated cyclone anomaly. Specifically, the cyclone anomaly over the WNP induced by CP El Niño aligns with the cyclone anomaly over the Yellow Sea induced by the warm SSTAs over the KE region, merging into a meridionally stretched cyclone anomaly to the east of EC. Consequently, the northerly anomalies stretch across EC, leading to unfavorable atmospheric conditions and the rainfall deficit there. Projection results show that the occurrence probability of a 2019Drought-like event will increase by 20% (decrease by 40%–50%) under a high (medium-low) emission scenario compared to present-day climate, indicating the nonlinear response of extreme drought to different emission scenarios and the urgency of carbon emission reduction. Significance Statement: An extreme drought hit the Eastern China (EC) region in 2019 and caused tremendous losses. This study proposed that both the 2019 CP El Niño and the warm SST anomalies over the Kuroshio Extension (KE) region induce the meridionally elongated circulation anomalies and the resultant extreme drought. We showed that the typical circulation anomalies induced by central Pacific (CP) El Niño cannot totally explain the meridionally elongated circulation anomalies in 2019. Our modeling evidences confirmed the indispensable role of warm SST anomalies over KE region in the 2019 extreme drought's formation. The projection results show that extreme drought like that in 2019 will occur more (less) frequently under a high (medium-low) emission scenario compared to modern-day level, indicating the urgency of carbon emission reduction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The central China summer precipitation (CCSP) is of great importance to the people's livelihood of this densely populated region, including the agriculture, ecosystems, water resources, economies, and health. Based on the observed precipitation, sea surface temperature (SST), and atmospheric reanalysis datasets, the present study investigates the effects of El Niño in the developing stage on the CCSP during 1960–2014. The results show that the CCSP anomalies exhibit significant negative correlations with the El Niño-related SST anomalies in both the simultaneous summer and the following winter, implying that the developing El Niño is important for modulating the CCSP. However, this climatic teleconnection of El Niño is unstable, with an obvious interdecadal change around the late 1980s. Specifically, the negative correlation is not statistically significant in the previous epoch before the late 1980s (1960–1988), but dramatically strengthens since the late 1980s (the post epoch for 1989–2014). Such an interdecadal change is closely associated with the change of the El Niño-related SST anomaly pattern. Compared to the previous epoch, the central Pacific El Niño occurs more frequently in the post epoch, leading to an interdecadal shift of the maximum warm SST anomalies from the eastern Pacific to the central Pacific. The resultant westward extension of the atmospheric circulation responses induces an anomalous low-level cyclone covering South China in the post epoch. It would prevent the southwest monsoon from delivering the moisture to the north and hence reduce the CCSP. While, in the previous epoch, the anomalous cyclone locates east of South China, exerting insignificant influence on the CCSP. This work highlights a strengthening effect of El Niño on the CCSP since the late 1980s, with great implications for the regional seasonal climate prediction. [ABSTRACT FROM AUTHOR]

Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB. [ABSTRACT FROM AUTHOR]

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 Indochina 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 westward 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 (1958–91). 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. Significance Statement: Spring precipitation variation has profound economic and societal influences for the Indochina Peninsula (ICP) that consists of several agriculture-based countries. Moreover, the local soil moisture anomaly induced by the ICP spring precipitation is an important seasonal predictor for the East Asian summer monsoon and extreme climate. Here this study identifies a strengthening influence of El Niño on the following spring precipitation over the ICP, due to a relatively stronger intensity and longer duration of the El Niño–related warm SST anomalies over the tropical central Pacific since the early 1990s. This may provide an important implication for the seasonal predictions of summer monsoon and extreme climate over East Asia and thus potentially enhance the predictability of regional climate. [ABSTRACT FROM AUTHOR]

During the summer following El Niño, a basinwide 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, the oceanic Rossby wave in the southern Indian Ocean, and the ENSO decaying rate. In contrast to El Niño events, La Niña events tend to have a stronger initial TIO warming and a lower 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 induces 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 basinwide SST cooling. These results have important implications for the climate predictability of the Indian Ocean and Asian summer monsoon. [ABSTRACT FROM AUTHOR]

The sea surface temperature anomalies (SSTAs) in the tropical Indian Ocean (TIO) show two dominant modes at interannual time scales, referred to as the Indian Ocean basin mode (IOBM) and dipole mode (IOD). Recent studies have shown that the IOBM and IOD not only affect the local climate, but also induce remarkable influences in East Asia via teleconnections. In this study, we assess simulations of the IOBM and IOD, as well as their teleconnections, using the operational seasonal prediction models from the Met Office (HadGEM3) and Beijing Climate Center [BCC CSM1.1(m)]. It is demonstrated that the spatial patterns and seasonal cycles are generally reproduced by the control simulations of BCC CSM1.1(m) and HadGEM3, although spectra biases exist. The relationship between the TIO SSTA and El Niño is successfully simulated by both models, including the persistent IOBM warming following El Niño and the IOD-El Niño interactions. BCC CSM1.1(m) and HadGEM3 are capable of simulating the observed local impact of the IOBM, such as the strengthening of the South Asian high. The influences of the IOBM on Yangtze River rainfall are also captured well by both models, although this teleconnection is slightly weaker in BCC CSM1.1(m) due to the underestimation of the northwestern Pacific subtropical high. The local effect of the IOD on East African rainfall is reproduced by both models. However, the remote control of the IOD on rainfall over southwestern China is not clear in either model. It is shown that the realistic simulations of TIO SST modes and their teleconnections give rise to the source of skillful seasonal predictions over China. [ABSTRACT FROM AUTHOR]

During 2016 boreal summer and fall, a strong negative Indian Ocean Dipole (IOD) event occurred, which led to large climate impacts such as the drought over East Africa. In this study, efforts are made to understand the dynamics of this IOD event and to evaluate real-time IOD predictions from current operational seasonal forecast systems. We show that both the wind-evaporation-SST and thermocline feedback lead to fast IOD growth in boreal summer 2016. Anomalous westerlies over the tropical Indian Ocean warmed the sea surface temperature (SST) over the tropical southeastern Indian Ocean (TSEIO) by reducing local evaporation; and wind induced thermocline deepening increased TSEIO SST by vertical advection. The intraseasonal disturbances in May induced the early subsurface warming and initiated the 2016 IOD. Due to negative cloud-radiation-SST feedback, the 2016 IOD event decayed quickly after October. We also demonstrate the successful real-time IOD predictions by the operational Hadley Center Global seasonal forecasting system version 5 (GloSea5) and the Beijing Climate Center Climate System Model (BCC-CSM1.1m). Resulting from the realistic representation of observed air-sea interactions, both models successfully predicted the evolution of the 2016 IOD up to 2 seasons ahead. The skillful prediction is also due to the precursor of the early subsurface warming in the eastern Indian Ocean, which increases intrinsic predictability of the 2016 IOD event. It is also demonstrated that IOD amplitude biases can be reduced by the joint-model prediction. The successful prediction of the 2016 IOD event allowed the East African drought to be predicted 4-6 months ahead. Our study reveals that current operational climate models can give useful warning of impending IOD events and impending climate extremes. [ABSTRACT FROM AUTHOR]

Under increased greenhouse gas (GHG) forcing, climate models tend to project a warmer sea surface temperature in the eastern equatorial Pacific than in the western equatorial Pacific. This El Niño-like warming pattern may induce an increase in the projected occurrence frequency of extreme El Niño events. The current models, however, commonly suffer from an excessive westward extension of the equatorial Pacific cold tongue accompanied by insufficient equatorial western Pacific precipitation. By comparing the Representative Concentration Pathway (RCP) 8.5 experiments with the historical simulations based on the Coupled Model Intercomparison Project phase 5 (CMIP5), a "present–future" relationship among climate models was identified: models with insufficient equatorial western Pacific precipitation error would have a weaker mean El Niño-like warming pattern as well as a lower increase in the frequency of extreme El Niño events under increased GHG forcing. Using this "present–future" relationship and the observed precipitation in the equatorial western Pacific, this study calibrated the climate projections in the tropical Pacific. The corrected projections showed a stronger El Niño-like pattern of mean changes in the future, consistent with our previous study. In particular, the projected increased occurrence of extreme El Niño events under RCP 8.5 forcing are underestimated by 30–35% in the CMIP5 multi-model ensemble before the corrections. This implies an increased risk of the El Niño-related weather and climate disasters in the future. [ABSTRACT FROM AUTHOR]