University of Birjand-Research Group of Drought and Climate Change Journal of Drought and Climate change Research 3092-6076 3 4 2026 02 20 Scale-Bridging Teleconnections and Local Extremes: Comprehensive Evaluation across 140 Stations Scale-Bridging Teleconnections and Local Extremes: Comprehensive Evaluation across 140 Stations 171 210 3815 10.22077/jdcr.2025.10474.1190 FA Alireza Saadatmoghadasi Department of Irrigation & Engineering; Reclamation Engineering.University of Tehran, Karaj, Iran 0009-0004-4096-1372 Zahra Aghashariatmadari Department of Irrigation & Engineering; Reclamation Engineering.University of Tehran, Karaj, Iran 0000-0002-9555-086X Javad Bazrafshan Department of Irrigation & Engineering; Reclamation Engineering.University of Tehran, Karaj, Iran. 0000-0002-6721-8990 Journal Article 2025 11 13 Teleconnections—statistically coherent, dynamically mediated couplings—shape Iranian temperature extremes across spatial and temporal scales. Leveraging a homogenized archive of monthly mean temperatures from 140 synoptic stations (1979–2023) alongside NOAA teleconnection indices, this study develops a phase‑lag‑aware ROCK–PCA framework that unites robust outlier handling, complex Hilbert embeddings and varimax‑type rotation with ERA5‑based composite diagnostics. The method isolates interpretable axes that map onto canonical modes (AMO/AMM/TNA, ENSO, PDO, EP–NP/PNA, NAO/EAWR, AO/SCAND) and quantifies their seasonally contingent leads/lags for Iran’s topographically diverse subregions. Results reveal an Atlantic‑led control: AMO/TNA dominate annual and warm‑season variability, peaking at two–three‑month leads; EP–NP emerges as the principal monthly‑scale driver at short lags; NAO/EAWR exert immediate wintertime synoptic influence; AO/SCAND contribute weeklies. Station‑resolved loadings highlight a southeast–east cluster (RPC7) during summer, implicating the Iranian thermal low and monsoon‑adjacent circulations, while lagged maps indicate a subsequent pivot toward the Persian‑Gulf littoral and, at longer lags, a sign reversal along the Caspian–Zagros windward belt. Extremal behavior, characterized via peaks‑over‑threshold statistics, exhibits systematic reorganization under positive AMO/AMM and TNA, with compounded warm‑season heat risk in southeastern Iran. Cross-validation between the rotated principal components and the gridded field composites, together with rank-based dependence metrics (e.g., Spearman/Kendall), demonstrates the robustness of our results despite known near-surface reanalysis biases. The framework delivers a minimal, maximally predictive subset of teleconnections and actionable lead‑time windows for sub‑seasonal‑to‑seasonal outlooks, thereby supporting risk‑aware planning across energy, agriculture, and public. Teleconnections—statistically coherent, dynamically mediated couplings—shape Iranian temperature extremes across spatial and temporal scales. Leveraging a homogenized archive of monthly mean temperatures from 140 synoptic stations (1979–2023) alongside NOAA teleconnection indices, this study develops a phase‑lag‑aware ROCK–PCA framework that unites robust outlier handling, complex Hilbert embeddings and varimax‑type rotation with ERA5‑based composite diagnostics. The method isolates interpretable axes that map onto canonical modes (AMO/AMM/TNA, ENSO, PDO, EP–NP/PNA, NAO/EAWR, AO/SCAND) and quantifies their seasonally contingent leads/lags for Iran’s topographically diverse subregions. Results reveal an Atlantic‑led control: AMO/TNA dominate annual and warm‑season variability, peaking at two–three‑month leads; EP–NP emerges as the principal monthly‑scale driver at short lags; NAO/EAWR exert immediate wintertime synoptic influence; AO/SCAND contribute weeklies. Station‑resolved loadings highlight a southeast–east cluster (RPC7) during summer, implicating the Iranian thermal low and monsoon‑adjacent circulations, while lagged maps indicate a subsequent pivot toward the Persian‑Gulf littoral and, at longer lags, a sign reversal along the Caspian–Zagros windward belt. Extremal behavior, characterized via peaks‑over‑threshold statistics, exhibits systematic reorganization under positive AMO/AMM and TNA, with compounded warm‑season heat risk in southeastern Iran. Cross-validation between the rotated principal components and the gridded field composites, together with rank-based dependence metrics (e.g., Spearman/Kendall), demonstrates the robustness of our results despite known near-surface reanalysis biases. The framework delivers a minimal, maximally predictive subset of teleconnections and actionable lead‑time windows for sub‑seasonal‑to‑seasonal outlooks, thereby supporting risk‑aware planning across energy, agriculture, and public. https://jdcr.birjand.ac.ir/article_3815_143758ee65fb29d30caa170c0db0ed36.pdf