Forecasts of standardized precipitation index phases over Iran by statistical-dynamical models

Abstract Sea-surface temperature anomalies (SSTA) and precipitation forecasts from the National Centers for Environmental Prediction-Climate Forecast System Version 2 (NCEP-CFSv2) model are combined to produce probabilistic forecasts of standardized precipitation index (SPI) phases at the seasonal time scales over Iran. The hybrid statistical-dynamical models are developed by statistical modeling drought probability relies on the combination of retrospective and operational forecasts from NCEP-CFSv2 model. Observed October-December (OND) and January-March (JFM) SPI phases are used to characterize meteorological droughts for the period 1982–2019. Ordinal regression models are applied to estimate the probability of drought phases. The results indicate that the SSTA forecasts over the central and eastern parts of the Pacific Ocean in comparison with precipitation forecasts generally produce higher skill for forecasts of OND SPI phases. The forecasting metrics for OND (JFM) season indicate that the developed models are able to forecast dry (wet) and normal phase better than wet (dry) phase in Iran. The forecast skill for OND SPI phases is generally higher than JFM. In comparison with precipitation forecasts as the individual predictor, the combination of the eastern Mediterranean SSTA and precipitation forecasts produces a significant and higher skill for JFM SPI phases for lead-times of 1 and 2 months. Accordingly, the developed hybrid models improve the probabilistic forecasts of SPI phases over Iran.