# Artificial intelligence pathways from weather to climate

arXiv: https://arxiv.org/abs/2610.09770 · HTML: https://mlsanity.com/paper/2610.09770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 171 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 30 of 32)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [Skillful joint probabilistic weather forecasting from marginals](https://mlsanity.com/paper/2506.10772/) (2025)
- [FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale](https://mlsanity.com/paper/2507.12144/) (2025)
- [ACE: A fast, skillful learned global atmospheric model for climate prediction](https://mlsanity.com/paper/2310.02074/) (2023)
- [ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction](https://mlsanity.com/paper/2402.00712/) (2024)
- [OneForecast: A Universal Framework for Global and Regional Weather Forecasting](https://mlsanity.com/paper/2502.00338/) (2025)
- [Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution](https://mlsanity.com/paper/2304.07029/) (2023)
- [Prithvi WxC: Foundation Model for Weather and Climate](https://mlsanity.com/paper/2409.13598/) (2024)
- [GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations](https://mlsanity.com/paper/2412.15687/) (2024)
- [Generative Diffusion-based Downscaling for Climate](https://mlsanity.com/paper/2404.17752/) (2024)
- [SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators](https://mlsanity.com/paper/2509.12490/) (2025)
- [Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting](https://mlsanity.com/paper/2601.18111/) (2026)
- [Numerical models outperform AI weather forecasts of record-breaking extremes](https://mlsanity.com/paper/2508.15724/) (2025)
- [Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings](https://mlsanity.com/paper/2504.09340/) (2025)
- [Robustness of AI-based weather forecasts in a changing climate](https://mlsanity.com/paper/2409.18529/) (2024)
- [EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules](https://mlsanity.com/paper/2509.26258/) (2025)
- [Evaluating the transferability potential of deep learning models for climate downscaling](https://mlsanity.com/paper/2407.12517/) (2024)
- [Exploring the design space of deep-learning-based weather forecasting systems](https://mlsanity.com/paper/2410.07472/) (2024)
- [CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score](https://mlsanity.com/paper/2510.09484/) (2025)
- [TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale](https://mlsanity.com/paper/2601.23268/) (2026)
- [Atmospheric Predictability Beyond 30 Days with Machine Learning](https://mlsanity.com/paper/2504.20238/) (2025)
- [Causal Climate Emulation with Bayesian Filtering](https://mlsanity.com/paper/2506.09891/) (2025)
- [FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution](https://mlsanity.com/paper/2506.03210/) (2025)
- [The Recipe Matters More Than the Kitchen:Mathematical Foundations of the AI Weather Prediction Pipeline](https://mlsanity.com/paper/2604.01215/) (2026)
- [Physics-Constrained Adaptive Flow Matching for Climate Downscaling](https://mlsanity.com/paper/2604.03459/) (2026)
- [Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators](https://mlsanity.com/paper/2602.16090/) (2026)
- [Advancing Subseasonal Forecasting with Machine Learning](https://mlsanity.com/paper/2604.16238/) (2026)
- [Regularization of ML models for Earth systems by using longer model timesteps](https://mlsanity.com/paper/2503.18023/) (2025)
- [A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models](https://mlsanity.com/paper/2604.21903/) (2026)

## Abstract (arXiv)

Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecasts at reduced cost. We review these advances and consider their extension to climate horizons, where the challenge shifts from initial-condition skill to producing reliable statistical responses under altered forcings. AI-powered climate prediction systems must produce credible forced responses to drivers (e.g., greenhouse gases, land-use change) typically outside the observed record. We propose two minimum requirements for AI in climate modeling: (i) external forcing agents must enter explicitly enough to support interventions in which they vary independently; and (ii) robustness must be stress-tested in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading AI autoregressive emulators and hybrid physics-AI models, we identify development and coupling challenges. Comparing the reported throughput of these models with that of GPU-ported dynamical models highlights how AI can reduce time-to-solution by advancing only the target variables at the required resolution and using longer time steps, rather than integrating a full high-frequency, multivariate state. Diverse AI downscaling strategies can partially substitute for explicit fine-scale resolution, paving the way toward inexpensive local hazard assessment across prediction horizons.