Our study utilizes a global reanalysis of near-surface daily air temperature data spanning the years from 1949 to 2019 to construct climate networks. By employing community detection for each year, we reveal the evolving community structure of the climate network within the context of global warming. Our findings indicate significant changes in measures such as network modularity and the number of communities over the past 30 years. Notably, the community structure of the climate network has undergone a discernible transition since the early 1980s. We attribute this transition to the substantial increase in isolated nodes since the 1980s, primarily concentrated in equatorial ocean regions. Additionally, we demonstrate that nodes experiencing amplified isolation tend to diminish connectivity with other nodes globally, particularly those within the same oceanic basin, while showing a significant strengthening of connections with the Eurasian and North African continents. We deduce that the mechanism driving amplified isolation in the climate network may be comprehended through the weakening of tropical circulations, such as the Hadley cell and Walker circulation, in response to increasing greenhouse gases.

The seasonal persistence barrier (PB) is a critical factor limiting the prediction skill of the El Niño-Southern Oscillation (ENSO). While previous studies have predominantly focused on the Niño3.4 region (Eastern Pacific El Niño), the increasing frequency of Central Pacific (CP) El Niño events since the 1990s necessitates a distinct examination of the Niño4 region, which exhibits unique predictability characteristics. Using long-term reanalysis datasets, this study investigates the PB of sea surface temperature (SST) in the Niño4 region. Climatologically, the PB for the Niño 4 index occurs during June–July, but it exhibits significant decadal variability. We identify that the Interdecadal Pacific Oscillation (IPO) plays a crucial role in modulating the timing of the PB (PB month). Our analysis reveals a robust relationship where the IPO phase regulates the seasonal locking of the prediction barrier. Specifically, during positive IPO phases, the PB tends to occur earlier, whereas distinct timing characteristics are observed during negative phases. Mechanistically, a diagnostic analysis based on the Bjerknes Stability Index (BJI) demonstrates that the IPO background state alters the seasonal cycle of the zonal advective feedback (ZA) in the equatorial central Pacific. This modulation shifts the seasonal peak of the total ENSO growth rate, thereby determining the timing of the steepest decline in prediction skill. These findings offer new insights into the decadal variability of CP-ENSO predictability and highlight the importance of background state modulation in ENSO forecasting.