<p>Tropospheric ozone time series consists of the effects of various scales of motion, from meso to large time scales which is often challenging for global models to capture them. This study uses two global datasets, namely reanalysis and analysis of the Copernicus atmospheric Monitoring Service (CAMS), to assess the capability of these models or systems in presenting ozone’s features in small scales. We employ the tropospheric ozone product of the models and in situ measurements at 18 stations over Iran for the year of 2020. Furthermore, we make use of data of ozone, temperature, nitrogen oxides, wind speed, and wind direction at one more station. We decompose the datasets into three spectral components, i.e., short (S), medium (M), and long (L) term. We only evaluate the S and M terms of modelled against those of observed datasets for all stations. We examine the relationship between ozone and the relevant proxies. Results show a correlation coefficient of larger than 0.5 for S and about 0.25 for M term in both models. It turns out that the reanalysis dataset demonstrates more precision for the S component than that for the M. Both models can show the observed correlation between ozone and temperature, whereas some inconsistencies appear in presenting the anti-correlation between ozone and nitrogen oxides.</p>