Interannual Variations in the Day‐to‐Day Temperature Variability in the Northern Hemisphere and Possible Causalities

Abstract Day‐to‐Day Temperature Variability (DTD) significantly influences public health and ecosystems, attracting growing scientific interest in its temporal characteristics. Although existing research has mainly concentrated on long‐term trends, comprehension of interannual‐scale DTD variations and their governing mechanisms remains inadequate. This investigation analyzes the spatiotemporal patterns of Northern Hemisphere DTD during 1961–2014, utilizing HadGHCND observational records and NCEP reanalysis data sets. Our findings indicate that the primary Empirical Orthogonal Function (EOF) mode demonstrates spatially consistent patterns across Eurasia and North America in both seasons, displaying a characteristic positive‐negative‐positive tripole configuration during summer. These dominant modes explain 18%–22% of summer variance and 40%–45% of winter variance, respectively. The thermodynamic equation term diagnosis establishes that horizontal temperature advection constitutes the fundamental physical process controlling these variability patterns. Winter DTD exhibits modulation through Arctic Oscillation (AO) influence across both continents, with North American patterns further affected by El Niño‐Southern Oscillation and Pacific‐North American teleconnection patterns. Conversely, summer DTD characteristics are principally determined by snow‐albedo‐temperature feedback mechanisms. This essential seasonal distinction emerges from the prevailing influence of large‐scale planetary circulation during winter versus localized thermal processes in summer. The recognized climate signals mainly impact DTD by modifying horizontal temperature gradients in mid‐high latitudes, thereby adjusting atmospheric baroclinicity, storm‐track intensity, and lower‐level temperature advection regimes. Assessment of eight CMIP6 climate models verifies their reliable capacity to replicate observed EOF patterns, while the multi‐model ensemble successfully reproduces related circulation anomalies. These outcomes considerably enhance our understanding of DTD dynamics and offer an important scientific foundation for climate risk mitigation.