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. Seasonal to annual forecasts of precipitation patterns are veryimportant for water infrastructure management. In particular, such forecastscan be used to inform decisions about the operation of multipurposereservoir systems in the face of changing climate conditions. Success inmaking useful forecasts is often achieved by considering climateteleconnections such as the El Niño–Southern Oscillation (ENSO) and IndianOcean Dipole (IOD) as related to sea surface temperature variations. Wepresent a statistical analysis to explore the utility of using rainfallrelationships in Sri Lanka with ENSO and IOD to predict rainfall to the Mahaweliand Kelani River basins of the country. Forecasting of rainfall as the classes flood, drought, and normal is helpfulfor water resource management decision-making. Results of these modelsgive better accuracy than a prediction of absolute values. Quadraticdiscrimination analysis (QDA) and classification tree models are used toidentify the patterns of rainfall classes with respect to ENSO and IODindices. Ensemble modeling tool Random Forest is also used to predict therainfall classes as drought and not drought with higher skill. These modelscan be used to forecast the areal rainfall using predicted climate indices.Results from these models are not very accurate; however, the patternsrecognized provide useful input to water resource managers as they plan foradaptation of agriculture and energy sectors in response to climate variability.

Identifying El Niño–Southern Oscillation influences on rainfall with classification models: implications for water resource management of Sri Lanka