Current information on broad-scale climatic conditions is essential for assessing potential distribution of forest pests. At present, sophisticated spatial interpolation approaches such as the Parameter-elevation Regressions on Independent Slopes Model (PRISM) are used to create high-resolution climatic data sets. Unfortunately, these data sets are based on 30-year normals and rarely incorporate up-to-date data. Furthermore, because they are constructed on a monthly rather than a daily time step, they do not directly measure simultaneous occurrence of multiple climatic conditions (e.g., days in the past year with appropriate temperature and adequate precipitation). Yet, the actual number of days—especially consecutive days—where multiple conditions are met could be significant for pest dispersal or establishment. For the sudden oak death pathogen ( Phytophthora ramorum ), we used National Oceanic and Atmospheric Administration daily weather station data to create current, national-scale grids depicting co-occurrence of multiple climatic conditions. For each station, we constructed two count-based variables: the total number of days and the greatest number of consecutive days in a year where the station met several conditions (temperature, rain/fog, relative humidity). We then employed gradient plus inverse distance squared (GIDS) interpolation to generate grids (4-km2 resolution) of these variables for 5 years (2000-2004). The GIDS technique weights standard inverse distance squared interpolation using coefficients based on geographic location (x, y) and a spatial covariate such as elevation. Using these variables, we determined the GIDS coefficients for each output grid cell via Poisson regression on the 30 closest stations. We also performed model selection to ensure only significant variables contributed to the GIDS coefficients. We compared the GIDS approach to cokriging and detrended kriging using cross-validation and found similar accuracies among all three interpolation methods. We also compared the output grids to maps assembled from the PRISM data depicting the probability all conditions were met in a given year. As expected, we found differences in areas highlighted as suitable for P. ramorum establishment by the two methods. We suggest that using current weather data and calculating the variable of interest directly will provide more practical information for mapping forest pest risk.