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Abstract In July and August 2022, the Yangtze River basin (YRB) experienced its hottest summer since 1961. The SINTEX‐F2 seasonal prediction system initialized in early May predicted the hotter‐than‐normal summer due to its successful prediction of central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the tropical West Pacific‐East Indian Ocean (TWP_EIO). The common SST forcing explains only about 26% to the heatwave strength, while the internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction account for approximately 65%, based on the analysis of 108 ensemble members. These factors have collectively increased the maximum temperature over the YRB through the enhancement and westward expansion of western North Pacific subtropical high. Our findings quantify the relative contributions of external forcing and internal variations to the unprecedented hot event, offering insights into its forming mechanism and potential predictability.

Abstract Accurate detection and attribution of past climate change are crucial for projecting future climate change and formulating proper policies. In this study, we show that the warming of the tropical Indian Ocean contributes to the observed wetting trend in the Tibetan plateau. The warming tropical Indian Ocean can lead to more precipitation around the Arabian Sea. The associated diabatic heating triggers the cyclonic atmospheric response in the lower troposphere over the Arabian Sea and eastern Africa. It also causes the enhancement and westward extension of the western North Pacific subtropical high. The in‐between airflow transports more moisture northward to the plateau, leading to the increased precipitation over the plateau. These large‐scale circulation patterns can be detected from the long‐term trends based on the observations and the large‐ensemble historical simulations. They can also be simulated by an atmospheric general circulation model forced by the observed warming merely in the tropical Indian Ocean.

Abstract The influence of Atlantic Niño on the following El Niño–Southern Oscillation becomes significant since mid‐1970s. However, exact mechanisms for this inter‐decadal change are still unclear. Here, we perform a set of model pacemaker experiments to probe the relative contributions of the changes in the Atlantic Niño itself and the mean‐state under global warming. The results suggest that the warmer background of the tropical Atlantic plays an essential role in enhancing local mean precipitation, inducing stronger divergence and low‐level easterlies in the Pacific. Under a favorable condition in the Pacific, even a weak Atlantic Niño‐related warming could promote the development of La Niña through cross‐basin Walker circulation and the Indian Ocean‐relayed Kelvin wave response. In contrast, the Atlantic Niño pattern change itself induces feeble convection anomalies in the western Atlantic, which cannot induce significant atmospheric response in the Pacific. These results imply an important modulation of global warming on the inter‐basin connection.

Abstract Investigating the Indian Ocean Dipole (IOD) during the Last Interglacial (LIG) can advance knowledge of IOD behaviors in orbitally‐induced warmer‐than‐present scenarios. Based on multiple model outputs from the Paleoclimate Modeling Intercomparison Project Phase 4, the analysis suggests reduced frequencies of the IOD compared to the preindustrial period. Spatially, the whole growing and mature stages of the IOD feature suppressed variability over the west and westward expansions of eastern anomalies, while the eastern perturbation enhances only at the initial phase. The overall amplitude assessed by the dipole mode index shows only minor reductions. These changes are attributed to orbitally‐induced enhancement in mean‐state westward currents along the equator, which transport more anomalous cold water westward. In addition, the subdued El Niño–Southern Oscillation (ENSO) during the LIG is unlikely to cause a weakening of the IOD, in association with a less important role of ENSO in the formation of the IOD.

Abstract The mid‐high latitude intraseasonal oscillation (ISO) has remarkable impacts on the Northern Hemisphere. However, the interannual variance of the mid‐high latitude ISO and its underlying mechanism are rarely explored. Here, we find that the tropical stratospheric Quasi‐Biennial Oscillation (QBO) has notable impacts. A statistically significant contrast in the interannual variability of intraseasonal 2‐m temperature (T2m) over northern Eurasia is detected between the easterly (EQBO) and westerly (WQBO) QBO phase. The influence of the QBO could be attributed to the interactions of ISO perturbation and QBO‐related winter‐mean flow. Specifically, the ISO perturbation is more efficient in extracting kinetic and potential energy from the winter‐mean flow in EQBO. Additionally, the northern Eurasia ISO propagates southeastward and exert effects on China. Due to this, the subseasonal forecast system of NUIST (NUIST CFS1.1) displays higher skill in predicting the T2m in China during EQBO phase at 3, 4, and 5 pentads in advance.

Abstract Deep learning (DL) has achieved notable success in El Niño‐Southern Oscillation (ENSO) forecasts. Most DL‐based models focused on forecasting ENSO indices while the zonal distribution of sea surface temperature anomalies (SSTA) over the equatorial Pacific was overlooked. To provide accurate predictions for the SSTA zonal pattern, this study developed a model through leveraging the merits of the cosine distance in constructing the convolutional neural network. This model can skillfully predict the SSTA zonal pattern over the equatorial Pacific 1 year in advance, remarkably outperforming current dynamical models. Moreover, the physical interpretation of the model prediction reveals that the sources for ENSO predictability at different lead times are distinct. For the 10‐month‐lead predictions, the precursors in the north Pacific, south Pacific and tropical Atlantic play critical roles in determining the model behaviors; while for the 16‐month‐lead predictions, the initial signals in the tropical Pacific associated with the discharge‐recharge cycle are essential.