In this study, we performed a long‐term ensemble hindcast from 1880 to 2017 (138 years) using the Community Earth System Model (CESM) and conducted a comprehensive investigation of the Indian Ocean Dipole (IOD) predictability. We found that the CESM can produce the IOD prediction skill comparable to that produced by some of the best state‐of‐the‐art coupled general circulation models, achieving a correlation skill of 0.5 for a lead time of one season over the 138 years. The Brier skill score shows one season of the effective probability prediction skill for the below‐ and above‐normal events and no probability prediction skill for the near‐normal events. The potential predictability of the IOD is much higher than the actual prediction skill; for example, the information‐based potential correlation is as high as 0.8 at a 6‐month lead time, suggesting a large scope for improvement in current IOD predictions. Compared with the dispersion component, the signal component dominates the variation in the relative entropy and the relationship between the potential predictability and deterministic prediction skill. An analysis of the IOD prediction skills suggests that the strength of IOD events plays an important role in the IOD prediction skills, regardless of the measurement metrics used to evaluate the prediction skills. Our study also suggested that the remote forcing from the tropical Pacific and the local sea‐air interaction in the tropical Indian Ocean would be two major sources of IOD predictability.

The concept of rapid growth (RG) of tropical cyclones (TCs) in the north Atlantic basin was recently proposed. RG can represent a dangerous change in TC structure because it can rapidly ramp up the TC destructive potential. However, the nature of RG behaviour remains obscure over the western north Pacific (WNP), where nearly one third of global TCs occur. In this study, TC RG in the WNP is investigated using TC best-tracks and reanalysis of data. We first define TC RG in the WNP as an increase of at least 84 km in the radius of a gale-force wind within 24 h, corresponding to the 90th percentile of all over-water changes. Monte Carlo experiments demonstrate the robustness of the threshold. Similar to that occurring in the north Atlantic, RG in the WNP is associated with the highest level of destructive potential. In addition, RG over the WNP occurs closer to the coast than for TCs in the Atlantic and more RG events in the WNP are accompanied by rapid intensification, which may significantly increase their destructive potential in a worst case scenario. Composite analysis shows that certain dynamic processes, such as radial inflow, may play an important role in the occurrence of RG. This study suggests that, apart from rapid intensification, TC RG is another important factor to consider for TC-related risk assessment in the WNP.

Precipitation prediction has been a challenging issue. The predictable pattern of Asian summer monsoon (ASM) precipitation is identified by the average predictable time method and precipitation hindcasts from the European Centre For Medium Range Weather Forecasts. The leading predictable pattern is characterized by a dipole pattern with the first center spanning from the northeast Bay of Bengal eastward to the western North Pacific, and the opposite center mainly in the Maritime Continent. It provides skillful prediction up to 21–24 days, far exceeding the skill averaged over the ASM (less than 5 days). Empirical mode decomposition analysis shows that the intraseasonal component, contributing 55% of the total variance of predictable components, provides the most predictability for ASM precipitation. The intraseasonal component originates from the boreal summer intraseasonal oscillation, which is enhanced by local ocean‐atmosphere interactions. The interannual component originating from the El Niño‐Southern Oscillation strengthens the predictability.

Considering the significant differences in the rainfall characteristics over East Asia between the early [May–June (MJ)] and late [July–August (JA)] summer, this study investigates the subseasonal predictability of the rainfall over East Asia in early and late summer, respectively. Distinctions are obvious for both the spatial distribution of the prediction skill and the most predictable patterns, that is, the leading pattern of the average predictable time (APT1) between the MJ and JA rainfall. Further analysis found that the distinct APT1s of MJ and JA rainfall are attributable to their different predictability sources. The predictability of the MJ rainfall APT1 is mainly from the boreal intraseasonal oscillation signal, whereas that of the JA rainfall APT1 is provided by the Pacific–Japan teleconnection pattern. This study sheds light on the temporal variation of predictability sources of summer precipitation over East Asia, offering a possibility to improve the summer precipitation prediction skill over East Asia through separate predictions for early and late summer, respectively.