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Dieng volcanic highland, where located in Wonosobo and Banjarnegara regencies, has a unique frost phenomenon that usually occurs in the dry season (July, August, and September). This phenomenon may attract tourism, but it has caused losses to farmers due to crop damage. Information regarding frost prediction is needed in order to minimize the negative impact of this extreme event. This study evaluates the potential use of the Subseasonal to Seasonal (S2S) forecast dataset for frost prediction, with a focus on two areas where frost usually occurs, i.e. the Arjuna Temple and Sikunir Hill. Daily minimum air temperature data used to predict frost events was from the outputs of the ECMWF model, which is one of the models contributed in the Subseasonal to Seasonal prediction project (S2S). The minimum air temperature observation data from the Banjarnegara station was used in conjunction with the Digital Elevation Model Nasional (DEMNAS) data to generate spatial data based on the lapse rate function. This spatial data was used as a reference to downscale the ECMWF S2S data using the bias correction approach. The results of this study indicated that the bias-corrected data of the ECMWF S2S forecast was able to show the spatial pattern of minimum air temperature from observations, especially during frost events. The S2S prediction represented by the bias-corrected ECMWF model has the potential for providing early warning of frost events in Dieng, with a lead time of more than one month before the event.
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