Evaluating the Impact of Post-Processing on Improving the Skill of Seasonal Ensemble Forecasts of Precipitation and Temperature of C3S Database in Iran

Seasonal precipitation forecasting plays a pivotal role in water resource management and development of warning systems. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran’s eight classified precipitation clusters for 1- to 3-month lead times. The quantile mapping (QM), the linear scaling (LS), and the gamma distribution mapping (GDM) for post-processing of precipitation forecasts, and the LS and variance scaling (VS) was used to post-process temperature forecasts. The results were then compared with the raw forecasts. It is indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. Almost all the post-processing methods were able to reduce the errors and improve the forecast accuracy in most groups. In general, after post-processing the ECMWF models had the best performance and the MF model had the worst performance. Among the precipitation post-processing methods, GDM and LS performed better, and the superiority of these methods is quite noticeable, especially in the rain-heavy groups of northern Iran (G6 and G8), which had poor raw forecasts. Regarding the post-processing of ensemble temperature forecasts, the performance of each LS and VS method is similar, they have a slight difference in increasing the accuracy of forecasts. Of course, overall, the VS method has worked a little better. The performance of post-processing methods is very effective in the cold months of the year (late autumn and winter) and slightly weaker in the hot months (summer).