A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.

A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events.

30-year record of Himalaya mass-wasting reveals landscape perturbations by extreme events.

In mountainous environments, quantifying the drivers of mass-wasting is fundamental for understanding landscape evolution and improving hazard management. Here, we quantify the magnitudes of mass-wasting caused by the Asia Summer Monsoon, extreme rainfall, and earthquakes in the Nepal Himalaya. Using a newly compiled 30-year mass-wasting inventory, we establish empirical relationships between monsoon-triggered mass-wasting and monsoon precipitation, before quantifying how other mass-wasting drivers perturb this relationship.