Beyond the Mean: Cold and Warm Tail Temperature Trends Reveal Asymmetric Controls on Snowpack Changes in the Northern Hemisphere

Conventional climate analyses rely on mean temperature trends to assess climate change impacts, yet this aggregation can obscure asymmetric shifts in the temperature distribution especially in threshold‐sensitive systems like snowpack. Here, we introduce a distributional diagnostic framework that decomposes winter temperature trends into median, cold‐tail (5th percentile), and warm‐tail (95th percentile) components across the Northern Hemisphere. Using 1981–2020 temperature records from the Berkeley Earth data set, we find that mean and median winter trends diverge substantially, with differences ranging from −0.19 to +0.41°C/decade (5th–95th percentile range) across snow‐affected grid cells. Mean trends systematically exceed median trends in 61% of locations, and these divergences are driven by spatially structured and climatology‐dependent tail behavior. We classify these patterns into four types based on directions of cold and warm tail trends relative to median trends and show that each aligns with distinct climatological regimes. Using multivariate regression, we show that median temperature trends consistently outperform means in explaining March snow water equivalent trends, while adding tail metrics further improves explanatory power. Tail contributions vary by climate: in extremely cold zones (−20°C) warm‐tail trends dominate, in moderately cold zones (−20 to −10°C) median trends and in near‐freezing regions (−10 to 0°C) cold‐tail trends. These results demonstrate that asymmetric distributional change is a key control on spring snowpack trends and highlight the need for percentile‐based diagnostics in climate impact assessments.