Multivariate deep-learning based regional climate model emulators and the impact of temporal awareness

Deep learning (DL) models have become popular methods to downscale low resolution climate data into high resolution climate projections, with the goal of avoiding the high computational cost associated with dynamical models like Regional Climate Models (RCMs). These DL-based downscaling models when applied in the context of RCMs and their Global Climate Model (GCM) counterparts, are referred to as RCM emulators.Currently, most DL based RCM emulators are single variate, which presents several drawbacks. For example, actual RCM's are multivariate and thus an RCM emulator should be as well. Additionally, a goal of these models is capturing extreme weather events, which are often multivariate as well. As such, this work explores the added value of multivariate emulators by testing four different DL-based RCM emulators (plus a single-variate emulator as baseline) at recreating a daily time series of 2D maps representing the average, maximum and minimum temperature on a given day at surface. All of these models rely on a U-Net based architecture. Notably, two of these DL models are considered to be ''temporal" (one of which implements a ConvLSTM architecture) as they both use multiple days worth of input data to make their predictions. These models are evaluated against a true RCM via several evaluation metrics, including general numerical metrics (RMSE, Correlation, etc.) as well as through real world applications, like the emulators ability to accurately represent future climate or reproduce heatwave events. We also implement a scheme of statistical significance testing via the Kruskal-Wallis method (with Dunn’s as post-hoc). Our results show that the temporal emulators, especially the LSTM model, consistently outperform the other models on a variety of the metrics. The results here support the theory that there is added value in not only making RCM emulators multivariate, but also that temporality improves the emulator's ability to make its predictions.