In this study, monthly average meteorological data and spatial coordinates are supposed to influence the climate variation. The geographical coordinates are transformed by using the Lambert projection method and are combined with the meteorological data. The complete data is standardised with zero mean and unit variance to remove the effect of different measurement scales. Various clustering methods are used to aggregate data, which are close in space and have similar climate. In our case, medoids clustering algorithm provides good separation and aggregation properties and if Lambert coordinates are used.