Weather-dependent renewable energy makes modern power systems more sensitive to extreme weather. Therefore

reliable identification of power system-oriented extreme weather scenarios is important for reliability assessment

resilience analysis

and risk-oriented operation. However

these scenarios are heterogeneous and suffer from severe class imbalance

which makes reproducible labelling and robust model training difficult. This paper proposes a hybrid classical-quantum learning framework for power system-oriented extreme weather scenario identification. First

a structured definition and labelling framework is developed to extract extreme weather scenarios from large-scale weather-power datasets by jointly considering meteorological abnormality

power system response

and external event evidence. Second

a quantum generative module is introduced as a training-only augmentation tool to synthesize minority-class samples

while validation and testing remain strictly based on real observations. Third

a hybrid classical-quantum classifier is constructed by embedding variational quantum circuits as trainable feature-mapping modules within a classical learning pipeline

aiming to examine their feasibility under small-sample and imbalanced-data conditions. In addition to conventional generation and classification metrics

risk-oriented indicators are used to evaluate whether the identified scenarios are meaningful for power system operation. The proposed framework is validated using real-world weather-power data from China. The results show that the quantum generative adversarial network preserves minority-class risk characteristics

while convolutional neural network-quantum recurrent neural network (CNN-QRNN) attains the numerically highest minority macro-F1 of 0.2998 among the compared models

with a macro-F1 of 0.3972.