Central southwest Asia (CSWA; 20°–47°N, 40°–85°E) is a water-stressed region prone to significant variations in precipitation during its winter precipitation season of November–April. Wintertime precipitation is crucial for regional water resources, agriculture, and livelihood; however, in recent years droughts have been a notable feature of CSWA interannual variability. Here, the predictability of CSWA wintertime precipitation is explored based on its time-lagged relationship with the preceding months’ (September–October) sea surface temperature (SST), using a canonical correlation analysis (CCA) approach. For both periods, results indicate that for CSWA much of the seasonal predictability arises from SST variations in the Pacific related to El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). Additional sources of skill that play a weaker predictive role include long-term SST trends, North Atlantic variability, and regional teleconnections. CCA cross-validation skill shows that the regional potential predictability has a strong dependency on the ENSO phenomenon, and the strengthening (weakening) of this relationship yields forecasts with higher (lower) predictive skill. This finding is validated by the mean cross-validated correlation skill of 0.71 and 0.38 obtained for the 1980/81–2014/15 and 1950/51–2014/15 CCA analyses, respectively. The development of cold (warm) ENSO conditions during September–October, in combination with cold (warm) PDO conditions, is associated with a northward (southward) shift of the jet stream and a strong tendency of negative (positive) winter precipitation anomalies; other sources of predictability influence the regional precipitation directly during non-ENSO years or by modulating the impact of ENSO teleconnection based on their relative strengths.

ABSTRACT:The Canonical Correlation Analysis (CCA) method has been used in this study for improving General Circulation Model (GCM) predicted rainfall over India during the southwest monsoon season. Hindcast runs for 27 years from six GCM outputs are used. This statistical technique relates the pattern of multivariate predictor field (model rainfall) to the pattern of predictand fields (observed rainfall). It is found that the CCA method improves the skill of three of the GCMs at the all-India level. A noticeable improvement is also observed in the composite prediction with CCA as compared to the simple mean of raw GCM products. The skill of the composite prediction after applying CCA is higher compared to the simple mean of raw model products in several homogeneous zones such as the hilly areas, west central area and over some parts of northwest India. The possible reason for the improvement in the skill of some of the GCMs may be the similarity between the loading patterns of model predictions and the observed rainfall.

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