Abstract. Rain on snow events (ROS) have the potential to cause wide-ranging ecological and societal impacts. Current knowledge and understanding of ROS and their future development are dependent on both observational and model datasets. However, different types of data provide insights into different aspects of ROS and carry limitations that may lead to contrasting results and conclusions, which need to be understood. This study examines the similarities and differences in ROS frequency over

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... inland Norway, estimated using a regional climate model (Weather Research and Forecasting (WRF)), a remote sensing method (Synthetic Aperture Radar (SAR)), and a gridded observational method (seNorge). Similarities in the geographical occurrence of ROS were obtained from both the WRF model and seNorge, with highest ROS frequency located predominantly along the western and southern coastal regions from autumn through to early spring, but greater ROS activity in the WRF model over inland mountainous areas during late spring and summer. We found significant differences in the spatial occurrence of ROS detected using the remote sensing approach, with much fewer ROS occurrences along the western coast but many more events inland from late autumn through to spring. Ground observations indicated the WRF model has an average accuracy for ROS detection of > 80 % for the period studied due to a high rate of detection of non-ROS days and low rate of false positives. However, the WRF model also missed on average > 50 % of the ROS days detected in ground-based data. For the SAR datasets, both the correct detection and false detection of ROS days was greater, producing a lower overall accuracy of 50–60 %. On the other hand, the timing of wet snow occurrence detected by SAR agreed qualitatively well with the onset of ROS detected from ground-based data, but the overall duration of a ROS event was frequently overestimated by SAR due to the persistence of liquid water in the snowpack. The similarities and differences across modeling and observational datasets shown in our study suggests that cross data validation is necessary and there is a need to analyse data collected at a much greater number of sites and future studies should take this into account.