The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operandocharacterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions─so-called operandomodeling─is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operandomodeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operandomodeling, followed by selected experimental examples that present an operandounderstanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.