<![CDATA[Extreme rain events pose challenges regarding disaster preparedness. Accurate rain prediction is one of the contributions to reducing the impact of extreme events. Predictions on subseasonal to seasonal (S2S) have been developed to fill the gap between short-term weather predictions and long-term seasonal forecasts. This study aims to assess the performance of the S2S model in predicting extreme rainfall events with extreme indices R95p, R99p PRCPTOT, and Rx1day. The data used are the S2S ECMWF data and observational data that were tested at the Stasiun Klimatologi Sumatera Barat and Stasiun Meteorologi Minangkabau during 2017-2022 period. Bias correction of S2S ECMWF data is corrected using the Distribution Mapping method. The results showed that the correlation value at Stasiun Klimatologi Sumatera Barat for daily rainfall ranged from 0.16 to 0.47 and ranged from 0.05 to 0.86 for monthly rainfall. Corrected model data correlation values at the Stasiun Meteorologi Minangkabau ranged from 0.24 to 0.41 for daily rainfall and ranged from 0.27 to 0.62 for monthly rainfall. The RMSE value at the Stasiun Klimatologi Sumatera Barat is smaller than Stasiun Meteorologi Minangkabau. The calculated extreme indices show underestimated values for the R95p, R99p, and overestimated values for the PRCPTOT, and Rx1day.]]>
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