The prolonged 2012–2016 California drought has raised many issues including concerns over reduced vegetation health. Drought impacts are complicated by geographical differences in hydroclimatic variability due to a climatic dipole influenced by the Pacific. Analysis of MODIS‐derived Normalized Difference Vegetation Index and self‐calibrated Palmer Drought Severity Index from 2000 to 2018 reveals differences in drought and vegetation responses in Northern versus Southern California (NorCal vs SoCal, see definition in section ). The greatest declines in Normalized Difference Vegetation Index were focused in the SoCal, while NorCal appears not severely affected thus far. It appears that both the strength of drought and the sensitivity of the vegetation to drought are larger in SoCal. The exacerbated aridity in SoCal is a trend extending throughout the past and present century. The spatial differences in hydroclimatology and vegetation responses are important considerations for statewide climate change adaptation—with SoCal potentially facing greater challenges.

Southern California is a biodiversity hotspot and home to over 23 million people. Over recent decades the annual wildfire area in the coastal southern California region has not significantly changed. Yet how fire regime will respond to future anthropogenic climate change remains an important question. Here, we estimate wildfire probability in southern California at station scale and daily resolution using random forest algorithms and downscaled earth system model simulations. We project that large fire days will increase from 36 days/year during 1970–1999 to 58 days/year under moderate greenhouse gas emission scenario (RCP4.5) and 71 days/year by 2070–2099 under a high emission scenario (RCP8.5). The large fire season will be more intense and have an earlier onset and delayed end. Our findings suggest that despite the lack of a contemporary trend in fire regime, projected greenhouse gas emissions will substantially increase the fire danger in southern California by 2099.

Accurate snow cover data are critical for understanding the Earth’s climate system, and exploring hydrological processes and regional water resource management over High Mountain Asia (HMA). However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54–98.08%, a low range underestimation error (MU) of 0.15–3.49% and an acceptable range overestimation error (MO) of 0.84–5.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52–92.40%, the omission error (OE) of 1.42–10.28% and the commission error (CE) of 5.97–17.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region.