Automated forecasting provides the basis for everyday forecast products used by a wide range of users. Continued progress in numerical weather prediction allows to produce local forecasts with considerable accuracy. To further reduce systematic errors and thereby render such local forecasts more beneficial to users, statistical postprocessing can be employed. While statistical postprocessing can readily be optimized for specific applications, optimization is less straight forward for general-purpose forecasts that are used across a diversity of applications and decisions. This issue is illustrated with ensemble postprocessing for automated precipitation forecasts. While medium-range precipitation forecasts are often communicated with hourly granularity, beyond the nowcasting range most applications are likely less affected by the precise timing of precipitation. In contrast, (sub-)daily aggregated precipitation may be a more relevant quantity. In addition, predictability of hourly precipitation is generally very limited days in advance and statistical postprocessing for hourly precipitation forecasts will therefore be strongly affected by the regression-to-the-mean problem (i.e. statistical postprocessing resorts to issuing climatological forecasts in the absence of predictability). To overcome the above issues, we propose a combined postprocessing approach operating on daily aggregated precipitation and hourly fractions of daily precipitation. We present results for a simple disaggregation according to the NWP precipitation and disaggregation according to a separate postprocessing of the hourly fraction of daily totals. The latter approach allows us to correct systematic biases in the diurnal cycle of precipitation occurrence particularly relevant for convective situations in complex topography as is the case in Switzerland. The same approach can be used to extend daily precipitation forecasts into the sub-seasonal to seasonal range.
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