A native-grid cover–depth benchmark reveals coupled snow-state biases in CMIP6 over the Tibetan Plateau

Abstract. Snow over the Tibetan Plateau is often widespread in winter but remains shallow over much of the region. This makes snow-covered area and snow depth complementary, not interchangeable, measures of model skill. We develop a monthly native-grid cover–depth benchmark for 2002–2014, evaluating CMIP6 historical simulations and major reanalysis products against MODIS-derived snow-covered area percentage (SNC) and CHE snow depth (SD). The reference fields are transferred to each product’s native grid before bias calculation, so that snow-state errors are diagnosed on the spatial support of the evaluated product. CMIP6 shows a dominant coupled snow-state excess over the Plateau. Median SNC biases reach 37.7 %, 30.2 % and 24.1 % in January, February and March, respectively, while SD bias is also positive but strongly skewed by several large outliers. In the January–March cover–depth bias space, 19 of 21 CMIP6 models overestimate both SNC and SD. The largest coupled biases occur over the western and high-elevation Plateau, indicating a structured cold-season error rather than a domain-wide offset. Reanalysis products show different forms of mismatch: ERA5-Land overestimates both SNC and SD, whereas MERRA-2 combines underestimated SNC with overestimated SD. Forcing diagnostics suggest that cold-biased persistence, snowfall input, snowfall partitioning and snow-process errors all contribute to the model spread. A cover–depth benchmark is therefore needed before Tibetan Plateau snow products are used in cryospheric, hydrological or land–atmosphere studies.