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The loss of autocorrelations of tropical sea surface temperatures (SST) during late spring, also called the spring predictability barrier (SPB), is a factor that strongly limits the predictability of El Nino Southern Oscillation (ENSO), and especially the statistical SST‐based ENSO forecasts starting from the winter‐spring season. Recent studies show that Pacific atmospheric circulation anomalies in winter‐spring may have a long‐term impact on the summer tropical climate via the SST footprint. Here, we infer an index based on sea level pressure (SLP) data from February to March in a single area surrounding Hawaii, and show that this area is the most informative part of the large SLP pattern initiating the SST footprinting mechanism. We then construct a statistically optimal linear model of the Nino 3.4 index taking this atmospheric index as a forcing. We find that this forcing efficiently lowers the SPB and provides significant improvements of interseasonal Niño 3.4 forecasts. Plain Language Summary: Interseasonal forecasting of El Niño Southern Oscillation (ENSO) is in high demand due to the impacts of ENSO on regional climatic conditions around the world as well as the global climate. Improvements in the quality of climate data in recent decades have led to the active use of statistical ENSO models, which compete with physical models in predictive power. The main disadvantage of statistical forecasts is the pronounced seasonal growth of uncertainty when predicting the upcoming summer‐fall ENSO conditions from winter‐spring months; this phenomenon is called the spring predictability barrier (SPB). A number of recent works revealed that winter‐spring atmospheric anomalies can substantially impact the ENSO system through the SPB via a complex atmosphere‐ocean interaction mechanism. Here, we introduce a reliable ENSO predictor constructed from sea level pressure data relating to this mechanism and show that the predictor significantly improves the multimonth (up to 1 year) ENSO forecast by lowering the SPB in a statistical model of the key ENSO index. Key Points: A novel early El Nino Southern Oscillation (ENSO) predictor based on the February–March sea level pressure is introducedSignificant correlations of the predictor with the upcoming summer – next spring ENSO conditions are shownThe predictor significantly improves the interseasonal forecast skills of the statistical Nino 3.4 index model [ABSTRACT FROM AUTHOR]