Majid Javari

Hydroclimatic non-stationarity has increasingly altered rainfall behavior across semiarid regions, reducing forecast reliability and increasing uncertainty in drought and water-resource management. This study investigated spatiotemporal changes in rainfall regimes across Iran using monthly precipitation records from 140 synoptic and climatological stations during 1980–2018. Adaptive statistical forecasting approaches were evaluated alongside machine-learning models using rolling-window validation based on RMSE, MAD, and MSE.The results showed a widespread decline in rainfall persistence across Iran, particularly in central and southern basins, where prolonged drought conditions intensified after the late 1990s. Northern and northwestern regions exhibited increasingly irregular rainfall behavior and greater interannual variability. Among the evaluated approaches, XGBoost achieved the highest forecasting accuracy, with an average prediction error (RMSE) of 18.2 mm month−1, outperforming conventional exponential smoothing by approximately 19%. The analysis further showed that even modest reductions in rainfall persistence can produce disproportionate declines in runoff generation across semiarid basins, substantially increasing uncertainty in reservoir inflow estimation, irrigation scheduling, and drought-risk planning. These findings highlight the importance of adaptive hybrid forecasting frameworks for improving hydroclimatic resilience under increasingly unstable rainfall regimes.

Climate change is intensifying heat exposure worldwide and is expected to increase the burden of cardiovascular diseases (CVD), particularly in semi-arid u

Understanding human thermal comfort is essential for assessing environmental conditions and their implications for well‐being, particularly in the context of global climate change. This study examines the influence of 30 climatic and ecological factors, including temperature, humidity, atmospheric pressure, solar radiation, wind dynamics, and topographical characteristics, on human thermal comfort across Iran. A multidisciplinary approach was employed, integrating principal component analysis (PCA) for feature selection, multivariate regression (MR) for impact quantification, cluster analysis (CA) for climate classification, and spatial modeling (SMA) to assess regional disparities. Furthermore, machine learning models (MLMs) and artificial neural networks (ANNs) were utilized to capture complex, nonlinear relationships in climate–comfort interactions. Based on a comprehensive data set spanning 38 years (1984–2022), the findings reveal significant spatial variations in climate sensitivity. Weighted indices such as predicted mean vote (PMV), physiologically equivalent temperature (PET), and thermal discomfort index (TDI) enhance the precision of comfort assessments. The results indicate that northern Iran, particularly the western coastal region of the Caspian Sea, exhibits the most favorable climatic conditions, whereas arid and semi‐arid areas experience heightened thermal stress. These insights advance biometeorological research by linking climate variability to human physiological responses and provide practical implications for urban planning, public health policies, and climate adaptation strategies. By integrating high‐dimensional climate data with advanced computational techniques, this study highlights the necessity of adaptive measures to mitigate the impacts of climate change on human thermal comfort.

Climatic comfort, a pivotal element in elevating human well-being and health, is influenced by an intricate network of meteorological variables. This investigation employs a structural equation modeling (SEM) approach, specifically partial least squares, alongside spatial modeling, and machine learning to scrutinize the multi-faceted impact of various cli-matic constructs on composite indices of climatic comfort across Iran. The constructs under examination encompass thermal, radiative, humidity, wind, and pressure elements, as well as distinct extreme climatic phenomena. Thermal constructs such as diurnal and annual temperature ranges, dew point temperature, and annual minimum and maxi-mum temperatures are analyzed within the model, alongside radiation constructs, including shortwave radiation and albedo. Similarly, humidity and wind constructs, represented by variables like relative humidity, precipitation, and wind speed and direction, are incorporated. Furthermore, specific climatic events, such as summer days, warm nights, and the Warm Spell Duration Index (WSDI), enhance understanding of the climatic conditions shaping thermal comfort. Composite comfort indices, including the Predicted Mean Vote (PMV), Universal Thermal Climate Index (UTCI), and Wet Bulb Globe Temperature (WBGT), serve as the ultimate criteria for comfort assessment in the model. Modeling outcomes reveal that thermal conditions (with a coefficient of 0.515) and extreme climatic occurrences (with a coefficient of 0.381), notably summer days, warm nights, and heat spell duration, exert the most pronounced positive and direct influence on the composite climatic comfort indices. These findings corroborate the primary role of elevated temperatures and extreme events in engendering thermal discomfort within Iran, particularly in southern and southeastern regions (e.g., the Per-sian Gulf and Oman Sea coasts), which experience the highest intensity of these phenomena. Conversely, solar radiation demonstrated a moderate inverse effect (-0.298), while humidity conditions (-0.074) and wind/pressure (0.043) exhibited weaker impacts on comfort. The model's substantial explanatory power (R2=0.811) and robust predictive capability (Q2=0.620) underscore its high efficacy in elucidating and forecasting climatic comfort variations. Moreover, the com-plete alignment of spatial patterns derived from comfort index maps (PMV, UTCI, WBGT, Humidex, TDI) and extreme event maps (Summer Days, Warm Nights, WSDI) with the model's results validates the accuracy and credibility of the findings in pinpointing areas with significant thermal challenges (south and southwest) and regions with more favora-ble comfort levels (mountainous and northern areas). Consequently, the interplay of these constructs and their reciprocal effects plays a crucial role in shaping climatic comfort conditions across diverse Iranian locales. This research can offer substantial guidance for policymakers in devising mechanisms to enhance climatic quality and manage urban and re-gional climatic change.

The trend and regimes of rainfall considerably are different effects on the bio-environmental process. Therefore; climatic elements changes and changed trends and regimes of rainfall are both makers, with severe changes to bio-environmental conditions. In a more detailed analysis, it is essential to detect both trends and regimes of rainfall, distribution of monthly and annual rainfall, as well as seasonality patterns. Therefore, this study used monthly and annual rainfall series records for 1975–2019 from 140 synoptic stations and satellite data such as geopotential height, Southern Oscillation Index (SOI), Northern Oscillation Index (NOI), North Atlantic Oscillation (NAO) to detect the trend and regimes of rainfall in Iran. Statistical analysis with parametric and non-parametric tests for monthly and annual rainfall series was used to detect the rainfall patterns based on selecting the Mann–Kendall test (MK), Sen.’s slope method (SSM), and the t-student test at a 5% significance level. Based on the seasonality index (SI) method to detect the seasonality patterns, we recognized spatial patterns of the regimes of rainfall and trends of rainfall in Iran. Finally, we evaluated the trends and the regimes' patterns of rainfall spatially, the type of trends with decreasing and increasing patterns, and rainfall changes range with spatial statistics models and the temporal distribution. Results show the different slopes from -0.409 to -0.156 mm based on spatial statistics models with the south-north oriented with a total decreasing pattern for rainfall changes. Rainfall patterns show the different patterns temporally (non-trend, decreasing, and increasing) based on stations with different periods in Iran, which may relate this difference to temporal distribution in recorded periods. Based on the seasonality, rainfall regime distribution shows diversity in seasonality in rainfall from an extreme seasonality pattern, mainly in most rainfall in < 3 months pattern, markedly seasonal with a long dry season pattern, mainly seasonal pattern, and extreme seasonality pattern in Iran. Therefore, the results of this study, effects of trends, and rainfall regimes were shown on environmental planning in Iran.

This paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.