The most predictable components of the El Niño-Southern Oscillation (ENSO) evolution in real-time multi-model predictions are identified by applying an empirical orthogonal function analysis of the model data that maximizes the signal-to-noise ratio (MSN EOF). The normalized Niño3.4 index is analyzed for nine 3-month overlapping seasons. In this sense, the first most predictable component (MSN EOF1) is the decaying phase of ENSO during the Northern Hemisphere spring, followed by persistence through autumn and winter. The second most predictable component of ENSO evolution, with lower prediction skill and smaller explained variance than MSN EOF1, corresponds to the growth during spring and then persistence in summer and autumn. This result suggests that decay phase of ENSO is more predictable than the growth phase. Also, the most predictable components and the forecast skills in dynamical and statistical models are similar overall, with some differences arising during spring season initial conditions. Finally, the reconstructed predictions, with only the first two MSN components, show higher skill than the model raw predictions. Therefore this method can be used as a diagnostic for model comparison and development, and it can provide a new perspective for the most predictable components of ENSO.

This study examines characteristics of Meiyu during the Meiyu season from June 1^st to July 31^st for 1998‐2017 using two analyses (i.e., TRMM and GPCP) and four reanalysis datasets (i.e., MERRA2, ERA5, JRA55 and NCEP/DOE). The Meiyu mean precipitation from GPCP is slightly larger than TRMM but GPCP has smaller precipitation variance as compared with TRMM. MERRA2 and ERA5 are consistent with TRMM precipitation in terms of both mean and variance patterns, while JRA55 (NCEP/DOE) has smaller mean precipitation and variance in the middle and lower reaches of the Yangtze River in China (near southern Japan). For the interannual variability of the precipitation averaged over the Meiyu region, the correlation with TRMM from high to low is ranked by MERRA2, GPCP, JRA55, ERA5 and NCEP/DOE. For the Meiyu evolution, GPCP, MERRA2, ERA5 and JRA55 capture the gradual northward movement of the Meiyu belt from June 2^nd to July 10^th, as shown in TRMM, but NCEP/DOE does not. For the large‐scale meteorology conditions for the strong and weak Meiyu years, the relative humidity vertical profiles from the four reanalyses are analyzed. All reanalyses except NCEP/DOE show that the relative humidity is larger in the strong Meiyu years than that in the weak Meiyu years and relative humidity over Japan and South Korea is larger than that over the lower reaches of the Yangtze River. It is recommended that TRMM and MERRA2 reanalysis be used for the investigations of the Meiyu precipitation and variability and large‐scale conditions.