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The study presents a statistically based seasonal\udprecipitation forecast model, which automatically identifies suitable\udpredictors from globally gridded sea surface temperature (SST) and climate variables by means of an extensive data-mining procedure and explicitly avoids the utilization of typical large-scale climate indices. This leads to an enhanced flexibility of the model and enables its automatic calibration for any target area without any prior assumption concerning adequate predictor variables.\udPotential predictor variables are derived by means of a cell-wise\udcorrelation analysis of precipitation anomalies with gridded global climate\udvariables under consideration of varying lead times. Significantly\udcorrelated grid cells are subsequently aggregated to predictor regions by\udmeans of a variability-based cluster analysis. Finally, for every month and\udlead time, an individual random-forest-based forecast model is constructed,\udby means of the preliminary generated predictor variables. Monthly\udpredictions are aggregated to running 3-month periods in order to\udgenerate a seasonal precipitation forecast.\ud  \udThe model is applied and evaluated for selected target regions in central\udand south Asia. Particularly for winter and spring in westerly-dominated\udcentral Asia, correlation coefficients between forecasted and observed\udprecipitation reach values up to 0.48, although the variability of\udprecipitation rates is strongly underestimated. Likewise, for the monsoonal\udprecipitation amounts in the south Asian target area, correlations of up to\ud0.5 were detected. The skill of the model for the dry winter season over\udsouth Asia is found to be low.\ud  \udA sensitivity analysis with well-known climate indices, such as the El Niño–\udSouthern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the East\udAtlantic (EA) pattern, reveals the major large-scale controlling mechanisms of the\udseasonal precipitation climate for each target area. For the central Asian\udtarget areas, both ENSO and NAO are identified as important controlling\udfactors for precipitation totals during moist winter and spring seasons.\udDrought conditions are found to be triggered by a cold ENSO phase in\udcombination with a positive state of NAO in northern central Asia, and by\udcold ENSO conditions in combination with a negative NAO phase in southern\udcentral Asia. For the monsoonal summer precipitation amounts over southern\udAsia, the model suggests a distinct negative response to El Niño events

A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia