Explainable machine learning for climate change attribution and hotspot identification: spatial cross-validation analysis in South Asian Region.

Ram Chand, Saeeddudin Sheikh, Barkha Kanjwani, Saqia Bukhari, Harsh Kumar

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Ram ChandDepartment of Natural Sciences, The Begum Nusrat Bhutto Women University, Sukkur, Sindh, Pakistan. ram.chand2k11@yahoo.com.

Saeeddudin SheikhHyderabad Institute of Technology and Management Sciences, Hyderabad, Sindh, Pakistan.

Barkha KanjwaniDepartment of Information and Communication Technologies, The Begum Nusrat Bhutto Women University, Sukkur, Sindh, Pakistan.

Saqia BukhariDepartment of Human and Rehabilitation Sciences, The Begum Nusrat Bhutto Women University, Sukkur, Sindh, Pakistan.

Harsh KumarDepartment of Mechanical Engineering, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan.

Funding

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6 · The paper itself

Abstract

We present a machine learning framework for climate change attribution and hotspot identification, with emphasis on testing geographic transferability through spatial cross-validation. We evaluate eight ML models for explaining temperature anomalies in Sindh, Pakistan on 44 years of observations. We performed spatial transferability testing using Leave-One-District-Out Cross-Validation (LODO-CV) to assess whether learned relationships generalize to districts not seen during training. Gradient Boosting was the most successful algorithm with ([Formula: see text]) under LODO-CV, which indicates robust transferability across the region's diverse climatic zones. The modest difference between LODO-CV and random cross-validation ([Formula: see text]) is consistent with findings that temporal structure often dominates spatial structure in climate time series

Indexed as

Climate attributionClimate changeExplainable AIHotspot identificationMachine learningSpatial cross-validationTemperature anomaly

Identifiers

PMID42336871

DOI10.1038/s41598-026-57382-9

PMCPMC13578701

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.