Detalle de: Temporal and Spatial Relationships Between Climatic Indices and Precipitation Zones in Europe, Spain and Catalonia

This study focuses on identifying distinct precipitation zones across Europe, Spain and Catalonia, and second, examining how various large‐ and small‐scale climatic patterns affect the precipitation in these zones. Previous research has focused primarily on the relationships between individual climatic indices and precipitation in specific regions but has often overlooked the combined influence of multiple climate signals on precipitation variability. To address these issues, this study proposes the use of principal component analysis (PCA) as a multivariate analysis framework to investigate the complex relationships amongst multiannual precipitation patterns at different spatial scales, specifically in Europe, Spain and Catalonia. Distinct correlations amongst total annual precipitation occur in European countries, Spanish provinces and small Catalonian regions. Europe and Spain have five precipitation zones, whereas Catalonia has four. The calculated trends indicate a total precipitation reduction in the Iberian Peninsula, western Mediterranean and southwestern Europe, with a projected further decrease. Conversely, northern and central Europe anticipate normal to high precipitation tendencies. A second PCA application explores time and spatial correlations between precipitation zones and local/global climatic indices. The Southern Annular Mode, key Pacific teleconnections (PNA, TNA, WHWP, PACWARM and BEST) and confirmed Atlantic patterns (EA, NAO and AO) emerged as influential. The WeMO and MO indices showed the expected relevance at local spatial resolutions. Multivariate data analysis methods for two‐ or multidimensional datasets, which span multiple years and various spatial units (countries/provinces/regions), can extend the use of multivariate data analysis tools for correlation analysis over time in diverse geographical areas, including other continents, with varying spatial and temporal resolutions. The inclusion of monthly average precipitation data as an additional dimension in datasets analysed by multivariate statistical methods, such as PCA, will improve the knowledge of spatiotemporal climate variability.