  El Ni\~no episodes are part of the El Ni\~no-Southern Oscillation (ENSO),
which is the strongest driver of interannual climate variability, and can
trigger extreme weather events and disasters in various parts of the globe.
Previously we have described a network approach that allows to forecast El
Ni\~no events about 1 year ahead. Here we evaluate the real-time forecasts of
this approach between 2011 and 2022. We find that the approach correctly
predicted (in 2013 and 2017) the onset of both El Ni\~no periods (2014-2016 and
2018-2019) and generated only 1 false alarm in 2019. In June 2022, the approach
correctly forecasted the onset of an El Ni\~no event in 2023. We show how to
determine the $p$-value of the 12 real-time forecasts between 2011 and 2022 and
find $p\cong 0.005$, this way strongly rejecting the null hypothesis that the
same quality of the forecast can be obtained by random guessing. We also
discuss how the algorithm can be further improved by reducing the number of
false alarms in the network model forecast. When combined with other
statistical methods, a more detailed forecast, including the magnitude of the
event and its type, can be obtained. For 2024, the method indicates the absence
of a new El Ni\~no event.
