Latent-Constrained Conditional VAEs for Augmenting Large-Scale Climate Ensembles
This study addresses the high computational cost of large climate model ensembles and the frequent need for additional statistically consistent spatiotemporal climate realizations in downstream analyses. To this end, the authors propose a latent-space-constrained conditional variational autoencoder (LC-CVAE), which aligns representations from multiple simulations in the latent space by sharing geospatial anchor points, thereby effectively mitigating the latent space fragmentation commonly observed in standard CVAEs. The framework further integrates multi-output Gaussian process regression to enable interpolation in the latent space, followed by decoding to generate full spatiotemporal fields. Experimental results demonstrate that high-quality synthetic climate fields can be achieved using only around five climate simulations, while also revealing a trade-off between spatial coverage density and reconstruction accuracy.