Statistically downscaled and bias corrected climate time series for 1000 randomly selected CR2MET grid cells

This database contains time series for 1000 grid cells located in Continental Chile that was used to characterize the relative importance of methodological decisions in removing biases and model projections in climate change impact assessments. The grid cells were randomly selected from the CR2MET (v2.0) meteorological product (Boisier et al., 2018; DGA, 2022, Boisier, 2023).

Particularly, we considered the following decisions:
i. Bias correction method (BCM).
ii. Choice of Global Climate Model (GCM).
iii. Temporal stratification (TS) used to apply the bias correction method.

We focus on the TS decision since it is usually overlooked in climate change studies. Additionally, we conduct a climate clustering to evaluate differences among different climates.

We also evaluate the GCMs' capability to replicate the historically observed precipitation seasonality and its interplay with the choice of TS. To this end, we compute the Taylor Skill Score (TSS; Taylor, 2001).