How Well Do Reanalyses Capture Day-to-Day Temperature Variability?

Day-to-day temperature variability (DTD) significantly affects human health and ecosystems, yet its representation in major reanalysis datasets has not been systematically evaluated. This study assesses the ability of four widely used reanalysis datasets, namely ERA-Interim, ERA5, JRA-55, and MERRA-2, against station observations to capture DTD’s spatial and temporal characteristics. All four datasets broadly reproduce the observed spatial pattern of DTD but generally underestimate its magnitude globally, except over eastern China. JRA-55 performs better at low-to-mid latitudes, while other datasets show closer agreement with observations at high latitudes. Regarding long-term trends, the reanalyses generally capture the observed pattern of decreasing DTD at high latitudes and increasing DTD at mid-low latitudes, but they show trends opposite to observations in summer over Eurasia, the low latitudes, and the Southern Hemisphere. Skill is highest in winter and lowest in summer, with ERA5 and ERA-Interim performing the best overall. Using ERA5 for further analysis, it is suggested that the recent weakening in global extreme DTD intensity is offset by an increase in extreme-event frequency, with both exhibiting substantial regional and seasonal variability. These findings advance understanding of short-term temperature variability and provide guidance for risk assessment, early warning, and mitigation.