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Due to the expansion of the decarbonization in modern power systems, particularly the high integration of renewable energy resources, more and more uncertainties and variabilities are brought to electricity systems. These uncertainties change the system operating conditions and face the power system with unknown issues. The secure operation of modern power systems requires developing security assessment techniques that effectively mitigate the variation of the operating conditions. This paper proposes a novel continuous learning scheme for online static security assessment (SSA) that enables the periodic renovation of the assessment model. The scheme consists of a Mondrian forest (MF)‐based training model and database generation approach conducting characteristics of operating conditions. The employed security index is a new weather‐depended performance index whose ambient temperature can affect line ratings. After the new training set is generated in the update procedure, the MF algorithm is updated using this data. Eventually, the SSA model is enriched with current circumstances in the power system. The proposed scheme is examined using real‐world data mapped to the New England 39‐bus and 118‐bus standard test systems. The effectiveness and superior performance of the proposed method are demonstrated using various criteria and measures.