This paper mainly focuses on investigating the influence of weather conditions on the sensitivity analysis and optimization of a typical passively designed high-rise residential building. A holistic passive design approach combining a variance-based factor prioritizing and surrogate model based multi-objective optimization was previously proposed to explore the green building solution in the hot and humid climate of Hong Kong. The design approach is further extended for application into a broader spectrum of climates across the mainland of China, including the severe cold zone, cold zone, hot summer cold winter zone, temperate zone as well as hot summer warm winter zone, The relative weight analysis is first compared with the Fourier Amplitude Transformation Analysis (FAST) in prioritizing the weighting of design inputs for different climatic zones. The relative weight analysis is then proved a feasible alternative sensitivity analysis method when its corresponding multiple linear regression (MLR) model can achieve good prediction performance. Furthermore, a tuning program in R is developed to improve the prediction performance of surrogate models with the Support Vector Machine (SVM) algorithm under above climatic zones. The model fitting performance with SVM is proved to be greatly improved by modifying the Sigma and C parameters. Finally, optimum design options under the five climatic zones are discussed in relation to the outdoor thermal, ventilation and solar radiation conditions. This research explored the applicability of the proposed passive design optimization approach in diverse climates, and can therefore prompt decision-makers' endorsement as a national green building design tool in the early planning stage.