The Urban Heat Island (UHI) phenomenon significantly affects us by exacerbating heat discomfort, increased energy consumption, and urban air pollution. The severity of UHI associated with heat waves and heat stress-related mortality is now one of the major concerns, particularly in densely populated cities. Therefore, the significance of the UHI has emerged as a crucial issue due to cities’ fast growth and urban development, necessitating a comprehensive review of the present status of UHI research, particularly concerning regional comparisons. This paper delineates the characteristics of UHI, focusing on its intensity, impact, determining factors, and potential mitigation strategy. It synthesizes an insight into important aspects of UHI from a comprehensive analysis of over 400 national/international research articles. The findings indicate a lack of UHI research studies in the central and eastern regions of the country, bringing out the need for further investigation in these areas. The observed UHI intensity varies across the country between 2 and 10 °C, with the northwest seeing a more pronounced temperature gradient. Following a detailed review, a few suggestions for future research to minimize the impact of UHI on public health, energy consumption, and the economy are proposed, as are strategies for mitigation. While studies on UHIs in India have primarily relied on observational data, there is still a substantial need for more research on employing numerical model-based and machine-learning approaches. Furthermore, the availability of mitigation research on Indian cities is limited. Additional research is needed to ascertain the intricate mechanisms behind the UHI effect on cities vulnerable to various climatic risks and hazards.

The COVID-19 lockdown restrictions influenced global atmospheric aerosols. We report aerosol variations over India using multiple remote sensing datasets [Moderate Resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), Cloud-Aerosol Lidar, and Infrared Pathfinder (CALIPSO)], and model reanalysis [Copernicus Atmosphere Monitoring Service (CAMS)] during the lockdown implemented during the COVID-19 pandemic outbreak period from March 25 to April 14, 2020. Our analysis shows that, during this period, MODIS and CALIPSO showed a 30–40% reduction in aerosol optical depth (AOD) over the Indo-Gangetic Plain (IGP) with respect to decadal climatology (2010–2019). The absorbing aerosol index and dust optical depth measurements also showed a notable reduction over the Indian region, highlighting less emission of anthropogenic dust and also a reduced dust transport from West Asia during the lockdown period. On the contrary, central India showed an ∼12% AOD enhancement. CALIPSO measurements revealed that this increase was due to transported biomass burning aerosols. Analysis of MODIS fire data product and CAMS fire fluxes (black carbon, SO₂, organic carbon, and nitrates) showed intense fire activity all over India but densely clustered over central India. Thus, we show that the lockdown restrictions implemented at the government level have significantly improved the air quality over northern India but fires offset its effects over central India. The biomass-burning aerosols formed a layer near 2–4 km (AOD 0.08–0.1) that produced heating at 3–4 K/day and a consequent negative radiative forcing at the surface of ∼−65 W/m² (±40 W/m²) over the central Indian region.

Surface solar radiation (SSR) is a fundamental energy source for an equitable and sustainable future. Meteorology-induced variability increases uncertainty in SSR, thereby limiting its reliability due to its intermittent nature. This variability depends on several meteorological factors, including clouds, atmospheric gases, and aerosol concentrations. This research investigates the detailed impact of different levels of clouds and aerosols on SSR across India. Utilizing satellite data with reanalysis retrievals, the research covers a span of three decades (30 years), from 1993 to 2022. Aerosols contributed to an average attenuation of ~13.33% on SSR, while high, mid, and low cloud conditions showed much stronger impacts, with an attenuation of ~30.80%, ~40.10%, and ~44.30%, respectively. This study reveals an alarming pattern of increasing cloud impact (C_impact) on SSR in the recent decade, with a significant increasing rate of ~0.22% year⁻¹ for high cloud (HC_impact) and ~0.13% year⁻¹ for mid cloud (MC_impact) impact, while low cloud impact (LC_impact) showed minimal change. The trend of aerosol impact (A_impact) also showed an average increase of ~0.14% year⁻¹ across all regions. The findings underscore the imperative of considering climatic variables while studying the growing solar dimming. Our findings also will assist policymakers and planners in better evaluating the solar energy resources across India.

Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (K_t). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m²), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m²; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes.

The implementation of a nationwide lockdown to curb the spread of COVID-19 disease has reduced the loading of anthropogenic aerosols. However, AOD distribution over South Asia during the lockdown period shows a dipole pattern: reduction over North Indian and enhancement over the Myanmar region. This dipole pattern is evident in some datasets (MODIS, MERRA, and CALIPSO). MODIS fire counts collocated with CALIPSO smoke aerosols show enhancement over Myanmar indicating the contribution from fires. However, over the North India region number of fires during the lockdown period are less compared to climatology. Thus, the observed reduction in AOD is due to fires and anthropogenic sources. Our analysis shows that aerosols originating from biomass burning forms a layer (900–600 hPa) over the Myanmar region that produces atmospheric heating (0–2.8 K/day) that eventually leads to cloud dissipation/burning (negative in-atmospheric cloud radiative forcing ∼ −13 W/m²) and precipitation reduction (−1 to −4 mm) over Myanmar. In contrast, the aerosol reduction over North India favors cloud formation, that is, increase in cloud cover and reduction in specific cloud liquid water content leading to precipitation enhancement, indicating the anti-Twomey effect.