Over the last two decades there have been diverse variabilities and trends in anthropogenic, pyrogenic, and biogenic emissions, resulting in varied responses of net ozone production rates (PO₃) to their precursors. A quantitative understanding of the underlying ozone precursor sources and the extent to which they influence PO₃ typically requires running computationally demanding chemical transport models, often constrained by satellite observations, under various modeling scenarios.Instead, we provide a much more efficient approach to predicting PO₃ using a deep neural network trained on more than 6 million observationally constrained data points collected from suborbital atmospheric composition missions. The parameterization meaningfully captures the non-linear relationships between O₃-NOX-VOC, as well as photolysis rates and water vapor. The parameterization inputs are constrained by various datasets including reanalysis models, ground remote sensing, TROPOMI and OMI retrievals, enabling us to provide a long-term record of global net ozone production rates along with magnitude-dependent sensitivity maps that advance beyond the conventionally binary maps (i.e., NOX-sensitive or VOC-sensitive) obtained from ozone indicators such HCHO/NO₂.We reveal predominantly positive trends in PO₃ over Asia and the Middle East (>30% relative to 2005) and negative trends across the eastern U.S., Europe, and parts of Africa during 2005-2019, based on stable long-term records from OMI. We demonstrate how rapid evolution of heat waves can substantially increase PO₃ and its sensitivity to NOₓ and VOC. Our high-resolution TROPOMI-based product reveals high locally-produced ozone in less-documented regions such as Johannesburg (South Africa), Rio de Janeiro (Brazil), São Paulo (Brazil), Santiago (Chile), Hanoi (Vietnam), Cairo (Egypt), and Tehran (Iran). This product, along with a comprehensive error budget, is freely available to our community (
ozonerates.space)