  We present a data-driven framework for ground-motion synthesis that generates
three-component acceleration time histories conditioned on moment magnitude,
rupture distance , time-average shear-wave velocity at the top $30m$
($V_{S30}$), and style of faulting. We use a Generative Adversarial Neural
Operator (GANO), a resolution invariant architecture that guarantees model
training independent of the data sampling frequency. We first present the
conditional ground-motion synthesis algorithm (cGM-GANO) and discuss its
advantages compared to previous work. We next train cGM-GANO on simulated
ground motions generated by the Southern California Earthquake Center Broadband
Platform (BBP) and on recorded KiK-net data and show that the model can learn
the overall magnitude, distance, and $V_{S30}$ scaling of effective amplitude
spectra (EAS) ordinates and pseudo-spectral accelerations (PSA). Results
specifically show that cGM-GANO produces consistent median scaling with the
training data for the corresponding tectonic environments over a wide range of
frequencies for scenarios with sufficient data coverage. For the BBP dataset,
cGM-GANO cannot learn the ground motion scaling of the stochastic frequency
components; for the KiK-net dataset, the largest misfit is observed at short
distances and for soft soil conditions due to the scarcity of such data. Except
for these conditions, the aleatory variability of EAS and PSA are captured
reasonably well. Lastly, cGM-GANO produces similar median scaling to
traditional GMMs for frequencies greater than 1Hz for both PSA and EAS but
underestimates the aleatory variability of EAS. Discrepancies in the
comparisons between the synthetic ground motions and GMMs are attributed to
inconsistencies between the training dataset and the datasets used in GMM
development. Our pilot study demonstrates GANO's potential for efficient
synthesis of broad-band ground motions
