International Journal of Climate Conditions

Longterm daily temperature and rainfall data (2000–2024) for thirty-five locations ranging from ~350 to >4000 m amsl were obtained from the NASA POWER database. The variability and trends in maximum, minimum temperature and rainfall were worked out by using different statistical tools and Mann-Kendal test
trend analysis. The mean annual maximum temperature of the state was 20.3°C (CV = 3.9%), declining from 27.4°C in the low hills to 7.2°C at high elevations, and
exhibited a consistent increasing trend, with comparatively higher warming rates at higher altitudes (~0.10°C yr⁻¹) than in the lower (~0.01°C yr⁻¹). Seasonal pooled
summer and monsoon maximum temperatures were 22.7°C and 25.5°C, respectively, both showing positive trends.

The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, and unable to maintain an infrastructural level to accommodate it.

The present study investigated the monthly, seasonal, and annual cloud cover variability over two stations in the mid hills sub-temperate subhumid zone of Himachal Pradesh, by using Pearson’s correlation coefficient, Mann-Kendall (MK), and Sen’s slope estimator test. Daily data on cloud cover, sunshine hours, maximum and minimum temperature, morning and evening relative humidity, evaporation and rainfall for the period of 22 years (2001–2022) were used in the investigation.

The atmosphere operates as a vast and complex chemical reactor, where minute-scale transformations exert profound influence on planetary-scale climate stability. This research investigates the multi-scale coupling between reactive tropospheric chemistry and large-scale climate feedbacks, challenging traditional modeling approaches that often divorce chemical kinetics from dynamic processes. By integrating high-resolution chemical transport models (CTMs) with comprehensive Earth System Models (ESMs), we map the flow of energy and matter from the molecular initiation of photochemistry to global radiative forcing.

Accurate detection of climate anomalies is vital for disaster alleviation and policy making in a sustainable manner, but customary detection methods face the challenges of computational inefficiency and physical inconsistency. In this study, we propose a novel approach called Quantum-Optimized Fuzzy Physics-Informed Neural Networks (QFuzzy-PINNs), which integrates quantum computing, fuzzy logic, and physics-informed deep learning.

Background: Climate change is fundamentally reshaping the environmental parameters of global sport, posing unprecedented risks to athlete health, safety, and performance. As rising temperatures, frequent extreme heat events, and deteriorating air quality become the new normal, athletic environments from community fields to elite international arenas face an existential threat.