On the basis of the latest greenhouse warming experiment performed with the Max-Planck Institut coupled atmosphere/isopycnal ocean model (ECHAM4/OPYC) it is shown that not only the climate mean but also the statistics of higher-order statistical moments respond sensitively to greenhouse warming. In particular the El Niño–Southern Oscillation (ENSO) cycle obtains more energy, and a tendency toward cold events can be observed. These statistical changes are superimposed on an overall warming trend. It is suggested that this information can be used in order to refine climate change detection via the optimal fingerprinting strategy. An optimal spectral fingerprint is developed on the basis of linear perturbation theory of wavelet variances. In order to elucidate the potential of higher-order statistical moments in the climate change detection context the optimal spectral fingerprint technique is applied to the ECHAM4/OPYC greenhouse warming simulation. The results provide a rough estimate of the times... Abstract On the basis of the latest greenhouse warming experiment performed with the Max-Planck Institut coupled atmosphere/isopycnal ocean model (ECHAM4/OPYC) it is shown that not only the climate mean but also the statistics of higher-order statistical moments respond sensitively to greenhouse warming. In particular the El Niño–Southern Oscillation (ENSO) cycle obtains more energy, and a tendency toward cold events can be observed. These statistical changes are superimposed on an overall warming trend. It is suggested that this information can be used in order to refine climate change detection via the optimal fingerprinting strategy. An optimal spectral fingerprint is developed on the basis of linear perturbation theory of wavelet variances. In order to elucidate the potential of higher-order statistical moments in the climate change detection context the optimal spectral fingerprint technique is applied to the ECHAM4/OPYC greenhouse warming simulation. The results provide a rough estimate of the times...