Real-world passive radiative cooling requires highly emissive, selective, and
omnidirectional thermal emitters to maintain the radiative cooler at a certain
temperature below the ambient temperature while maximizing the net cooling
power. Despite various selective thermal emitters have been demonstrated, it is
still challenging to achieve these conditions simultaneously because of the
extreme complexity of controlling thermal emission of photonic structures in
multidimension. Here we demonstrated machine learning mediated hybrid
metasurface thermal emitters with a high emissivity of ~0.92 within the
atmospheric transparency window 8-13 {\mu}m, a large spectral selectivity of
~1.8 and a wide emission angle up to 80 degrees, simultaneously. This selective
and omnidirectional thermal emitter has led to a new record of temperature
reduction as large as ~15.4 degree under strong solar irradiation of ~800 W/m2,
significantly surpassing the state-of-the-art results. The designed structures
also show great potential in tackling the urban heat island effect, with
modelling results suggesting a large energy saving and deployment area
reduction. This research will make significant impact on passive radiative
cooling, thermal energy photonics and tackling global climate change