## 3 Results and Discussion
### Teleconnection Patterns  
This section shows the results of the spatial and statistical analysis between temperatures and nine teleconnection patterns. Figure 4 shows the percentage of meteorological stations with a statistically significant positive (+) or negative (-) correlation between the teleconnection patterns and the mean temperature in California. The results of the correlations of atmospheric teleconnection patterns and temperatures are presented in a heat map outlining the highest values in dark red and the lowest in light red, following previous investigations [53; 57; 85; 86; 87]. The general circulation of the atmosphere displays significant variability on many diverse time scales. The chosen modes of low-frequency atmospheric variability have been labelled as teleconnections [22]. They are the main cause of weather anomalies that occur for a long period over different regions. In addition, these teleconnections patterns have an effect on temperature and precipitation regimes [88]. Particularly, in our research, temperatures in February, March, April and May are highly correlated with most of the teleconnection patterns in the State of California. The Pacific Decadal Oscillation (PDO) is a teleconnection pattern of North Pacific sea surface temperature that alternates phases every 20-30 years [88]. A remarkable characteristic is that this index shows multiyear and multidecadal persistence with just a few signs of change [68]. Figure 5 shows that this pattern has a constant positive correlation with mean temperature. This pattern reveals that it might have more influence on the average temperature in California's coastal areas such as San Francisco, Monterey Bay, Los Angeles and San Diego. We can observe in the maps that the highest percentage of stations with positive correlation is found along the coast as previous studies have brought to the fore [18; 19]. This is most notable in May, when 19.2% of stations were statistically significant, followed by June (30%), September (33%) and October (38%). In March, when the percentage of stations with a significant correlation is the largest, there was no difference between coastal and interior locations in the State. The remarkable predominance of significantly positive correlations (Figure 4) allows us to say about PDO is probably that it is related to increases in average temperatures all over California in the period studied. Figure 3: Conf. Figure 3: Cont. Figure 3: Conf. Figure 3: Spatial distribution trends (positive in red, negative in blue) in mean temperatures (”C/year”) and their statistical significance on monthly, seasonal and annual time-scale. Area with positive statistical significance at 95% confidence level. Figure 4: Heatmap with the percentage of meteorological stations that have shown statistical significance positive (+) or negative (\(-\)) correlations between teleconnection patterns and average temperatures at 95% confidence level. Blank spaces are zero percentage values that had been removed to ease visualization. The top histogram (blue) represents the percentage contribution of the teleconnection patterns to the variance of temperature of the meteorological stations in each month. The right histogram (green) shows the percentage contribution of each teleconnection pattern for itself, in the whole year. PDO (Pacific Decadal Oscillation), PNA (Pacific-North American), WPO (Western Pacific Oscillation), EPO (East Pacific Oscillation), NAO (North Atlantic Oscillation), ENSO (El Niño–Southern Oscillation), RMM1 and RMM2 (Real Multivariate MJO), AO (Artic Oscillation) and AAO (Antarctic Oscillation). Figure 5: Percentage of meteorological stations in the State of California with significant correlations (positive in red, negative in blue) between PDO and temperature. The correlations between PNA pattern and temperature have been strongly positive in February (Figure 6). In this month, 82% of the stations all over California have shown a positive and statistically significant correlation.