Artificial Intelligence Techniques for Climate Change Prediction and Mitigation: A Systematic Review

Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art

Extreme rainfall events (ERES) are among the most challenging hydro-meteorological phenomena to forecast because the complex, nonlinear atmospheric processes involved span multiple spatial and temporal scales and are further intensified by rising climate variability. While Numerical Weather Prediction (NWP) models offer physically consistent representations of atmospheric dynamics, they still struggle to resolve localized convection, rapidly evolving storm systems, and rare, high-intensity precipitation events. This study systematically reviews recent advances in Artificial Intelligence (AI) for extreme rainfall prediction published between 2020 and 2026, using a transparent literature search strategy, predefined screening criteria, quality assessment, and a structured literature review matrix. The review synthesizes evidence from a final evidence base of 139 studies across several dimensions, including deep learning (DL) methodologies, multimodal data fusion, forecasting horizons, operational deployment, uncertainty quantification, and emerging intelligent forecasting paradigms. The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations. Despite notable progress, persistent challenges remain, including data scarcity, class imbalance, model generalization, physical consistency, uncertainty estimation, transferability, and the lack of standardized evaluation frameworks. This review provides a unified synthesis of current methodologies. It identifies key research gaps, highlighting the need for evaluation frameworks that address physical plausibility, uncertainty quantification, reliability, transferability, and operational relevance. The findings indicate that future extreme rainfall prediction systems should advance beyond accuracy-focused evaluation toward integrated, trustworthy, uncertainty-aware, and physically consistent forecasting approaches.

A CNN–LSTM Deep Learning Framework for Multivariate Temperature Forecasting and Extreme Weather Event Analysis

Throughout history weather predication has been a powerful and necessary tool in a number of activities that have had a major influence on human development and survival such as climate monitoring, agricultural planning and disaster management. Unlike other variables, temperature fluctuations are a major challenge for prediction due to the fact that they are nonlinear and extremely dynamic. This research paper introduces deep learning (DL) architectures for multivariate temperature forecasting using past weather data of single cities in India as samples. The DL methods such-as LSTM, GRU, Hybrid CNN-LSTM and Attention-based model were first outlined and then experimented. The meteorological variables that were considered include humidity, precipitation, wind speed and cloud cover. The quality of forecasting is quantified by the means of MAE, RMSE, MAPE and R2. Experimental results reveal that the DL methods achieve significantly better forecasting of temperature as compared to the conventional ARIMA model. Out of the deep learning models which were experimented with the CNN-LSTM model gave the best performance and has the following results: MAE (287.37), RMSE (401.60), MAPE (7.35%) and R2 (0.893). Besides that, CNN-LSTM model not only performed better in normal conditions but also in extreme temperatures and was able to achieve R2 of 0.923, which underscores its capacities to capture complex weather changes. This paper provides strong evidence of the value of combining DL methods for weather prediction. In the future, efforts will be made to enhance the accuracy of prediction by using transformer-based time-series models and incorporating larger spatiotemporal climate data.

Leveraging Machine Learning to Understand Climate and Extreme Event Impacts on Crop Yields: A Systematic Review (2015–2025)

Quantifying the impacts of climate change and extreme climatic events on crop yields is essential for safeguarding global food security. The rapid growth of data availability and advances in computational capacity have established machine learning (ML) as a critical tool for unraveling the complex, nonlinear relationships between climatic factors and agricultural productivity. This systematic review synthesizes evidence from 137 peer-reviewed studies published between 2015 and 2025 that applied ML models to assess the effects of both long-term climate trends and discrete extreme events on crop yields worldwide. Bibliometric and thematic analyses reveal a rapidly evolving field, with over 85% of studies published since 2020, and a strong concentration on staple cereals—wheat, maize, and rice—in major agricultural regions including China, the United States, and India. Random Forest (RF) was the most commonly used algorithm; ensemble and deep-learning models achieved high predictive accuracy within well-resourced study contexts. Temperature and precipitation extremes emerged as the most frequently examined stressors, with distinct methodological patterns: studies focusing on climate change trends predominantly employed RF and LSTM models, whereas those investigating extreme events increasingly adopted hybrid approaches that integrate ML with process-based crop models. This review highlights the transformative potential of ML while identifying persistent challenges, such as geographical imbalances in research coverage, the need for enhanced interpretability in extreme event attribution, and the critical importance of modeling compound extremes. Future research should prioritize the development of explainable, causally informed, and transferable ML frameworks to support equitable climate adaptation strategies in global agriculture.

Machine Learning for Heatwave Prediction: A Global Scoping Review of Environmental Predictors and Modelling Practices

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in parts of the world that are already subject to extreme heat, such as tropical regions. In recent years, machine learning (ML) has increasingly been applied to environmental and meteorological data to improve the prediction of heatwaves and extreme heat events. This scoping review examines global peer-reviewed literature on the application of ML techniques for extreme heat prediction using environmental variables. This includes heatwave prediction, environmental and meteorological predictors used in these models, and the geographical distribution of existing research. A total of 23 peer-reviewed studies meeting the inclusion criteria were included in the review, following the PRISMA-ScR guidelines. The findings indicate that artificial neural networks and random forest models were most frequently reported as high performing within individual studies. However, direct comparisons across studies are limited by heterogeneity in prediction targets, validation strategies, lead times, heatwave definitions, and performance metrics. Temperature-related variables, especially maximum temperature, were consistently identified as the most influential predictors across studies. Furthermore, the evidence base was heavily concentrated in Europe, Asia, and North America, with comparatively limited representation from low- and middle-income countries respective to population, despite these regions often experiencing disproportionate impacts of climate change and extreme heat exposure. By synthesising current evidence on ML-based heatwave prediction, associated environmental predictors, and geographical research trends, this review provides insights to support the development of more robust, context-aware, and globally representative heatwave forecasting frameworks.

An Improved AI Weather Prediction System

Weather prediction remains a critical challenge due to the nonlinear and dynamic nature of atmospheric systems, as traditional numerical weather prediction (NWP) models struggle to process large, high-dimensional meteorological data and often lack the adaptability needed for accurate short- and medium-term forecasts, particularly during extreme weather events. This study develops an improved AI-driven weather prediction system that enhances forecasting accuracy for temperature, humidity, wind speed, and atmospheric pressure through a Random Forest predictive model integrated with the Open Weather Map API and geolocation services. Using Agile methodology, data was collected, preprocessed, and trained in Python, while a web-based interface was built with JavaScript/TypeScript and React for visualization. The proposed system achieved approximately 87% short-term forecasting accuracy (87% applies to 1 – 7-day short term forecast), demonstrating improved precision and enhanced early-warning capability for extreme events. Comparative evaluation showed that, whereas the existing edge-based system was constrained by low processing power, maintenance overhead, and security concerns, the proposed AI system outperformed it in accuracy, adaptability, and real-time usability, offering significant benefits for agriculture, disaster management, and urban planning.

Predicting Thermal Structure in Large Reservoirs Under Climate Change Using Machine Learning