  The optimal fingerprint method serves as a potent approach for detecting and
attributing climate change. However, its experimental validation encounters
challenges due to the intricate nature of climate systems. Here, we
experimentally examine the optimal fingerprint method simulated by a precisely
controlled magnetic resonance system of spins. The spin dynamic under an
applied deterministic driving field and a noise field is utilized to emulate
the complex climate system with external forcing and internal variability. Our
experimental results affirm the theoretical prediction regarding the existence
of an optimal detection direction which maximizes the signal-to-noise ratio,
thereby validating the optimal fingerprint method. This work offers direct
empirical verification of the optimal fingerprint method, crucial for
comprehending climate change and its societal impacts.
