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<Article>
<Journal>
				<PublisherName>مؤسسه ژئوفیزیک دانشگاه تهران</PublisherName>
				<JournalTitle>فیزیک زمین و فضا</JournalTitle>
				<Issn>2538-371X</Issn>
				<Volume>51</Volume>
				<Issue>4</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>03</Month>
					<Day>17</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Assessing Teleconnections Effects on the Precipitation Seasonality at 44 Synoptic Stations across Iran</ArticleTitle>
<VernacularTitle>Assessing Teleconnections Effects on the Precipitation Seasonality at 44 Synoptic Stations across Iran</VernacularTitle>
			<FirstPage>207</FirstPage>
			<LastPage>231</LastPage>
			<ELocationID EIdType="pii">105305</ELocationID>
			
<ELocationID EIdType="doi">10.22059/jesphys.2025.401809.1007722</ELocationID>
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>Ali Reza</FirstName>
					<LastName>Saadat Moghadasi</LastName>
<Affiliation>Department of Irrigation and Reclamation Engineering, Faculty of Agriculture, College of Agriculture and Natural
Resources, University of Tehran, Karaj, Iran.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2025</Year>
					<Month>09</Month>
					<Day>04</Day>
				</PubDate>
			</History>
		<Abstract>Teleconnections—planetary‑scale conduits linking tropical diabatic heating to extratropical circulation—reconfigure jet structure, storm‑track geometry, and moisture transport, thereby modulating Iranian precipitation, which is sparse, highly seasonal, and sensitive to Mediterranean cyclogenesis. Using quality‑controlled 1991–2020 rainfall from 44 IRIMO synoptic stations and fourteen NOAA indices (AMO, AMM, AO, EAWR, EP‑NP, MEI, ONI, PDO, PNA, QBO30, SCAND, SOI, TNA, and TSA), we applied a hierarchical pipeline comprising canonical correlation analysis (CCA), seasonal partial least squares regression (PLSR), and k‑means regime clustering. CCA demonstrated coherent coupled variability between large‑scale modes and regional rainfall, justifying multivariate attribution. PLSR, calibrated separately for OND, JFM, and AMJ, yielded mean skill of R²=0.405 (0.155–0.746), 0.416 (0.203–0.888), and 0.287 (0.102–0.533), respectively, with one latent component sufficing at most stations but up to 12 required in the most teleconnection‑responsive winter sites. VIP diagnostics reveal a seasonal reordering of controls: AMO and EP‑NP, together with ENSO family indices, dominate JFM; SCAND is pre‑eminent in AMJ; and ENSO re‑intensifies during OND alongside EAWR. Station‑level maxima of |β| locate the strongest couplings, notably SOI/MEI over Kerman (|β|≈1.1–1.2, negative) and ONI over Kish (β≈−1.07) in winter, and PDO over Isfahan (β≈−0.56) in autumn. Clustering of normalized monthly fractions partitions stations into seven robust precipitation regimes (silhouette ≈0.62), separating Caspian bimodal climates, Zagros‑orographic spring peaks, and monsoon‑fringe southeastern tails. Collectively, results indicate that tropical Pacific forcing and Eurasian wave‑train modulation jointly shape Iran’s wet‑season predictability, while methodological pluralism is essential to retain low‑amplitude yet hydrologically consequential signals.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;</Abstract>
			<OtherAbstract Language="FA">Teleconnections—planetary‑scale conduits linking tropical diabatic heating to extratropical circulation—reconfigure jet structure, storm‑track geometry, and moisture transport, thereby modulating Iranian precipitation, which is sparse, highly seasonal, and sensitive to Mediterranean cyclogenesis. Using quality‑controlled 1991–2020 rainfall from 44 IRIMO synoptic stations and fourteen NOAA indices (AMO, AMM, AO, EAWR, EP‑NP, MEI, ONI, PDO, PNA, QBO30, SCAND, SOI, TNA, and TSA), we applied a hierarchical pipeline comprising canonical correlation analysis (CCA), seasonal partial least squares regression (PLSR), and k‑means regime clustering. CCA demonstrated coherent coupled variability between large‑scale modes and regional rainfall, justifying multivariate attribution. PLSR, calibrated separately for OND, JFM, and AMJ, yielded mean skill of R²=0.405 (0.155–0.746), 0.416 (0.203–0.888), and 0.287 (0.102–0.533), respectively, with one latent component sufficing at most stations but up to 12 required in the most teleconnection‑responsive winter sites. VIP diagnostics reveal a seasonal reordering of controls: AMO and EP‑NP, together with ENSO family indices, dominate JFM; SCAND is pre‑eminent in AMJ; and ENSO re‑intensifies during OND alongside EAWR. Station‑level maxima of |β| locate the strongest couplings, notably SOI/MEI over Kerman (|β|≈1.1–1.2, negative) and ONI over Kish (β≈−1.07) in winter, and PDO over Isfahan (β≈−0.56) in autumn. Clustering of normalized monthly fractions partitions stations into seven robust precipitation regimes (silhouette ≈0.62), separating Caspian bimodal climates, Zagros‑orographic spring peaks, and monsoon‑fringe southeastern tails. Collectively, results indicate that tropical Pacific forcing and Eurasian wave‑train modulation jointly shape Iran’s wet‑season predictability, while methodological pluralism is essential to retain low‑amplitude yet hydrologically consequential signals.</OtherAbstract>
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			<Param Name="value">Eurasia</Param>
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			<Object Type="keyword">
			<Param Name="value">Precipitation</Param>
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			<Param Name="value">Indices Correlation</Param>
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			<Param Name="value">Iran</Param>
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<ArchiveCopySource DocType="pdf">https://jesphys.ut.ac.ir/article_105305_9e8263ef622202b38e6f2b636946bbee.pdf</ArchiveCopySource>
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