The El Niño-Southern Oscillation (ENSO) has been shown to manifest as primarily two types, the eastern Pacific (EP) type and central Pacific (CP) type, in terms of the zonal positions of the sea surface temperature (SST) anomalies. This study focuses on examining the predictability of the two types of ENSO by developing statistical models for their corresponding Niño indices, which have their own distinct key precursors. The results show that the statistical predictability of the Niño indices representing the two types of ENSO primarily originates from the preceding variations in the equatorial Pacific upper-ocean heat content and the surface zonal wind stress, which intrinsically reflect the zonally uniform and contrasted thermocline patterns, respectively. The traditional Niño3 and Niño4 indices are more predictive than the Niño indices of the EP and CP ENSO types; however, all the indices are subject to predictability barriers with different timings and intensities, which might be weakened by introducing additional external precursors. The EP ENSO indices have overall higher skills than the CP indices, in which the statistical model has much higher skill scores than persistence forecast for the EP ones while it does less for the CP ones. We demonstrate that the precursors outside the tropical Pacific, e.g., the Indian Ocean Dipole, North Pacific oscillation, North American dipole, and Southern Hemispheric SST modes, except the northern tropical Atlantic SST, as suggested in previous studies, only make limited contributions to improving the prediction skills of the two ENSO types at specific initial months and leads compared to a benchmark model built using the equatorial Pacific heat content and zonal wind stress indices. This is primarily because these precursors have already transferred most of their signals into the variation of the two indices in the benchmark model. We further show that conditionally adding the northern tropical Atlantic SST precursor to the benchmark could provide considerable additional prediction skill scores for both types of ENSO and weaken the intensity of the ENSO predictability barriers that occur during boreal spring‒summer.

An anomalous “north‐south” dipole mode of the snow water equivalent (SWE) persisting from winter to spring is detected over the Eurasian mid‐to‐high latitudes in this study. Using observational data sets and numerical experiments of the Community Atmospheric Model (5.0), we show that this mode contributes to prolonged winter‐springtime coldness in midlatitude Eurasia and is closely linked to the declining November Arctic sea ice concentration. The decline in the sea ice concentration over the Barents‐Laptev Seas can induce a teleconnection pattern over the mid‐to‐high latitudes in the following winter, accompanied by an anomalous ridge over the Ural Mountains and an anomalous trough over Europe and East Asia. Such changes in the large‐scale circulation lead to more cold surges and heavy snowfall in the midlatitudes and light snowfall in the high latitudes, forming an anomalous north‐south dipole mode of the SWE, which further reduces the temperature through thermodynamic feedback. Due to seasonal memory, this SWE pattern can persist into the following spring and can lead to springtime midlatitude coldness via thermodynamic and dynamic processes. For the thermodynamic process, the anomalous SWE condition can lead to anomalous wet soil, reduced incoming surface solar radiation, and cooling air in the midlatitudes. This phenomenon induces an enhanced Siberian High and a deepened East Asian trough via the snow‐Siberian high‐feedback mechanism, which favors a cold spring in northern East Asia. Further analysis suggests that an empirical seasonal prediction model based on the SWE can reasonably predict East Asian spring temperature anomalies.

The subseasonal variability and predictability of the Arctic Oscillation/North Atlantic Oscillation (AO/NAO) is evaluated using a full set of hindcasts generated from the Beijing Climate Center Atmospheric General Circulation Model version 2.2 (BCC_AGCM2.2). It is shown that the predictability of the monthly mean AO/NAO index varies seasonally, with the highest predictability during winter (December–March) and the lowest during autumn (August–November), with respect to both observations and BCC_AGCM2.2 results. As compared with the persistence prediction skill of observations, the model skillfully predicts the monthly mean AO/NAO index with a one-pentad lead time during all winter months, and with a lead time of up to two pentads in December and January. During winter, BCC_AGCM2.2 exhibits an acceptable skill in predicting the daily AO/NAO index of ∼9 days, which is higher than the persistence prediction skill of observations of ∼4 days. Further analysis suggests that improvements in the simulation of storm track activity, synoptic eddy feedback, and troposphere–stratosphere coupling in the Northern Hemisphere could help to improve the prediction skill of subseasonal AO/NAO variability by BCC_AGCM2.2 during winter. In particular, BCC_AGCM2.2 underestimates storm track activity intensity but overestimates troposphere–stratosphere coupling, as compared with observations, thus providing a clue to further improvements in model performance

This article demonstrates that the above‐normal Tibetan Plateau snow depth (TPSD) in winter appears to be followed by an intensified Philippine Sea anticyclone in June (PSAC‐J), and vice versa. This linkage is clearly independent of the relationship between the El Niño‐Southern Oscillation (ENSO) and the PSAC‐J. Moreover, winter TPSD anomalies are typically associated with a PSAC‐J pattern that shifts northwards compared with that associated with ENSO. A better understanding of the combined effects of the winter TPSD and ENSO on the PSAC‐J could improve our ability to predict both the East Asia summer monsoon and variations in the Meiyu–Changma–Baiu rainbelt.