This paper describes a study focusing on performance evaluation of RWIS pavement temperature forecasts. To identify the factors influencing the accuracy of forecasts, five research hypotheses were constructed that RWIS forecasting accuracy would be affected by climatic patterns (e.g., maritime, continental, and mixed), locational attributes (e.g., geography), seasonal variations (e.g., shoulder months vs. non-shoulder months), time of day (e.g., day vs. night), and forecast horizon. RWIS observations and forecasts data sets provided by four North American provincial transportation agencies were pre-processed and stratified by station, hour, and month, to test the hypotheses and quantify their effects by utilizing two performance metrics, namely mean absolute error (MAE) and percent of acceptable forecasts (PAF). The overall statistics showed that maritime climate group had the highest correspondence and those from mixed climate group had the lowest correspondence, both in terms of their MAEs and PAF. As for the locational attributes, it was found that the forecasting performance of maritime region near coastal areas was found to have a negative correlation with the distance from nearby large water body. It was also found that daytime forecasts were less accurate than the ones generated for night time. Furthermore, the accuracy of forecasts was found to deteriorate quickly as the forecasting horizon increases. Lastly, forecast errors were found to exhibit seasonal variations with forecasts for the shoulder/transitional months tending to be poorer than other months.