arXiv:2606.30821v1 Announce Type: new Abstract: Probabilistic downscaling is the task of modeling the conditional distribution of high-resolution fields given coarse inputs, and is a central challenge to atmospheric science, climate modeling, and other multiscale physical systems. A widely used paradigm decomposes the problem into a deterministic mean predictor followed by a stochastic residual generator. While effective in idealized settings, this mean--residual approach frequently produces biased and under-dispersive ensembles in real-world applications. Is this merely generic predictive unc
The increasing availability of high-resolution data and the demand for more accurate predictive models in complex physical systems drive the focus on refining downscaling techniques.
Improving probabilistic downscaling, especially for real-world biases, is critical for accurate climate modeling, atmospheric science, and managing long-term resource planning implications.
Better understanding and mitigation of 'residual gap' and bias in downscaling models will lead to more reliable and actionable predictions for environmental and physical systems.
- · Climate scientists
- · Atmospheric modelers
- · Insurance industry
- · Policy makers
- · Organizations relying on simplistic predictive models
- · Sectors vulnerable to unmitigated climate risks
More accurate predictive models for environmental phenomena will be developed and deployed.
Improved climate modeling will lead to better-informed infrastructure planning and disaster preparedness strategies.
Enhanced model reliability could influence investment decisions in climate-resilient technologies and impact the valuation of physical assets.
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Read at arXiv cs.LG