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Purpose <p>To synthesize recent advances in artificial intelligence (AI) (machine learning (ML)/deep learning (DL)) for climate science across climate modeling, extreme-weather analysis, renewable-energy optimization, emissions monitoring, and climate adaptation.</p> Methods <p>Using a PRISMA-aligned protocol, we surveyed studies published between 2015 and 2025 across major scholarly databases. We extracted task definitions, datasets, model classes, evaluation metrics, baselines, validation design, uncertainty treatment, openness (code/data), and energy/CO<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(_2\)</EquationSource> </InlineEquation> reporting. Evidence is organized by application domain and by methodological rigor, emphasizing transferability and operational relevance.</p> Results <p>Strong evidence exists for improved short- to medium-range forecasting, nowcasting, bias correction, and anomaly detection. Physics-guided and hybrid models, including PINNs and neural operators, increasingly rival operational numerical weather prediction (NWP) systems in targeted settings. However, robust external validation, out-of-distribution testing, uncertainty quantification, and transparent reporting of code and computational footprint remain inconsistent.</p> Conclusions <p>AI enhances but does not replace physically based climate modeling. We contribute a field-level synthesis that combines PRISMA screening with a study-quality checklist to foreground evaluation rigor, reproducibility, and sustainability. Adoption of physics-guided architectures, probabilistic prediction, regime-aware validation, and carbon-aware benchmarking is essential for decision-grade climate AI.</p>

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Advances in artificial intelligence for climate science methods applications and evaluation challenges

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Essam H. Houssein,
  • Mahmoud Dirar,
  • Abdelmaged A. Ali,
  • Waleed M. Mohamed,
  • Kadry Hamed

摘要

Purpose

To synthesize recent advances in artificial intelligence (AI) (machine learning (ML)/deep learning (DL)) for climate science across climate modeling, extreme-weather analysis, renewable-energy optimization, emissions monitoring, and climate adaptation.

Methods

Using a PRISMA-aligned protocol, we surveyed studies published between 2015 and 2025 across major scholarly databases. We extracted task definitions, datasets, model classes, evaluation metrics, baselines, validation design, uncertainty treatment, openness (code/data), and energy/CO \(_2\) reporting. Evidence is organized by application domain and by methodological rigor, emphasizing transferability and operational relevance.

Results

Strong evidence exists for improved short- to medium-range forecasting, nowcasting, bias correction, and anomaly detection. Physics-guided and hybrid models, including PINNs and neural operators, increasingly rival operational numerical weather prediction (NWP) systems in targeted settings. However, robust external validation, out-of-distribution testing, uncertainty quantification, and transparent reporting of code and computational footprint remain inconsistent.

Conclusions

AI enhances but does not replace physically based climate modeling. We contribute a field-level synthesis that combines PRISMA screening with a study-quality checklist to foreground evaluation rigor, reproducibility, and sustainability. Adoption of physics-guided architectures, probabilistic prediction, regime-aware validation, and carbon-aware benchmarking is essential for decision-grade climate AI.