Using wavelet analysis, wavelet-based principal component analysis (WPCA), composite analysis and scale-averaged wavelet power (SAWP) of seasonal precipitation, we investigated their spatiotemporal variability, their relationships with seven large-scale climate indices and atmospheric circulation patterns, sea surface temperature (SST) of the Pacific and Atlantic oceans, and the predictability of Alberta's seasonal precipitation. June–August (JJA) precipitation showed statistically significant oscillations at about 16–20 year, while September–November (SON) precipitation at about 4–8 year time scale. Plots showing clusters of annular contour lines for positive and negative Morlet wavelet coefficients, respectively represent cycles of abundant and sparse seasonal precipitation that had occurred over 1900–2011. Wavelet power spectrum plots show that seasonal climate indices and principal component (PC1) of precipitation anomalies exhibited dominant oscillations that appeared and disappeared in an unpredictable manner. Wavelet coherence analysis shows that Alberta's precipitation had been linked more to Niño3, Pacific decadal Oscillation (PDO) and Pacific/North American (PNA) than other climate indices, and in the 8–25 year than inter-annual time scales. Similar results are found from Pearson's correlation between PC1 of band-passed, zero to three seasonal lags, of Alberta's seasonal precipitation and climate anomalies. Aggregate composites of Alberta's December–February (DJF) precipitation showed that the influences of El Niño Southern Oscillation (ENSO), PDO and PNA had been stronger in southern than in northern Alberta. El Niño (La Niña), warm PDO (cool PDO) and high PNA (low PNA) are associated mainly with sparse and abundant DJF precipitation of southern Alberta. Prevalent atmospheric circulation patterns provide physical explanations on the effects of climate anomaly on the DJF precipitation revealed by the composite analysis.