A general theory is proposed for the statistical correction of weather and climate forecasts based on observed analogs. An estimate is sought for the probability density function (pdf) of the observed state, given today's numerical forecast. Assume that an infinite set of reforecasts (retrospective numerical forecasts) and associated analyses are available, and that the climate is stable. Assume that it is possible to find a set of past model forecast states that are nearly identical to the current forecast state. With the dates of these past forecasts, the asymptotically correct probabilistic forecast can be formed from the distribution of observed states on those dates. If the analyses have higher spatial resolution than the forecasts, then the analog statistical correction also ‘downscales' the numerical forecast to the scale of the analyses. In this study we illusrate this technique using output from a global forecast model run at roughly 200 km resolution (representative of the current generation of climate models) to produce probabilistic precipitation forecast information at a scale of 32 km using precipitation analyses from the North American Regional Reanalysis.