A Novel Statistical Downscaling Methodfor Generating High-Resolution ClimateProjections for Europe from CMIP6

Reliable projections of Earth’s future climate are an essential source of information to better adapt to the impacts of climate change on societies and natural systems. Climate models provide information on the possible evolution of climate in the coming decades to centuries, however, this information has several limitations such as inadequate resolution to capture the fine-scale features that characterize hydroclimatic conditions at the local scale. Climate model output downscaling aims at partly addressing these limitations.

Here, we present a novel methodology to generate 5 km × 5 km climate information at the European scale based on CMIP6 model output, which not only corrects model biases locally, but also preserves large-scale climate features (spatial correlation) from the original climate model data.

Our approach builds from an existing downscaling technique: Bias-Corrected Constructed Analogues with Quantile Mapping Reordering. Compared to the BCCAQ implementation available in the well-known R package ClimDown, our methodology introduces two major differences:

Identification of Dynamically Coherent and Persistent Weather Regimes: We perform the daily analogue selection only for dynamically coherent and persistent days. This process begins by identifying large-scale circulation patterns. The first 10 principal components (PCs) of daily mean sea level pressure (MSLP) from both the CERRA reanalysis and the GCM are calculated. Then, a multivariate Hidden semi-Markov model (HSMM) is used to detect hidden states (representing meteorological regimes) in the GCM's data over the period 1950–2100. This allows for the identification of persistent blocks of at least five consecutive days characterized by a single dominant weather regime. Blocks shorter than five days, or those without a dominant regime, are excluded from the reordering step.

Targeted Analogue Search and Reordering: For each day within an identified block, the search for historical analogues in the CERRA data is conducted within a window of ±15 days from that calendar day, using a mean squared difference metric on the relevant variable. Finally, a "Schaake Shuffle" reranking of the corresponding Quantile Delta Mapping (QDM) daily outputs is performed within each identified block of continuous days using the identified climate analogues. This ensures the preservation of realistic temporal structure of the weather sequences across the coherent meteorological regimes.

Our downscaling method is calibrated with historical data (1985–2014) from the Copernicus European Regional Reanalysis (CERRA) and this calibration propagates the downscaling into the future for model simulations up to 2099 using the emission scenarios SSP245, SSP370 and SSP585 for the nine climate models and for the variables daily maximum (tasmax), minimum (tasmin), mean (tas) temperature and daily precipitation (pr).

The proposed methodology is portable and potentially applicable to any other region and/or set of input model data as well as an observational reference used to calibrate the model data.