According to the present invention, provided is a learning method for generating high-resolution weather and climate data, the learning method comprising the steps of: (a) for each of specific grid points from among grid points corresponding to respective points at which a plurality of first axes parallel to a first direction and a plurality of second axes parallel to a second direction intersect, when first past forecast data as a value corresponding to weather and climate at each of t-mth past time points is acquired by a predetermined first forecast model, performing learning on a first deep learning module; (b) for each of the specific grid points, receiving at least a part of first specific past forecast data corresponding to a specific past time point and specific future forecast data corresponding to a specific future time point of at least a part of future time points so as to, as a result of performing a first deep learning operation, output, as second deep learning input forecast data, a value corresponding to specific weather and climate for each of the specific grid points and for each of at least a part of the specific past time point and the specific future time point; and (c) performing learning on a second deep learning module while performing a predetermined pre-processing on second specific deep learning input forecast data and generating reduced data reduced to a predetermined first magnification.