High-resolution, bias-corrected climate data is necessary for climate impact studies at local scales. Gridded historical data is convenient for bias-correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data represent true climatological measurements, but as the distribution of climate stations is irregular, station data are challenging to ... [Show full abstract] incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias-correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980-1989 and 1980-2014) and two testing sets of RCM data (1990-2014 and 1980-2014). RMSE for all methods varied throughout the year and was larger in cold months, while PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980-1989 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method produced substantial improvements in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, while EQM is ideal for bias-correcting historical climate model output.