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Macro-economic assessments of climate impacts lack an analysis of the distribution of daily rainfall, which can resolve both complex societal impact channels and anthropogenically forced changes 1 – 6 . Here, using a global panel of subnational economic output for 1,554 regions worldwide over the past 40 years, we show that economic growth rates are reduced by increases in the number of wet days and in extreme daily rainfall, in addition to responding nonlinearly to the total annual and to the standardized monthly deviations of rainfall. Furthermore, high-income nations and the services and manufacturing sectors are most strongly hindered by both measures of daily rainfall, complementing previous work that emphasized the beneficial effects of additional total annual rainfall in low-income, agriculturally dependent economies 4 , 7 . By assessing the distribution of rainfall at multiple timescales and the effects on different sectors, we uncover channels through which climatic conditions can affect the economy. These results suggest that anthropogenic intensification of daily rainfall extremes 8 – 10 will have negative global economic consequences that require further assessment by those who wish to evaluate the costs of anthropogenic climate change. A global assessment shows that increases in the number of wet days and extreme daily rainfall adversely affect economic growth, particularly in high-income nations and via the services and manufacturing sectors.

Rainfall erosivity quantifies the effect of rainfall and runoff on the rate of soil loss. Maps of rainfall erosivity are needed for erosion assessment using the Universal Soil Loss Equation (USLE) and its successors. To improve erosivity maps that are currently available, hourly and daily rainfall data from 2381 stations for the period 1951–2018 were used to generate new R-factor and 1-in-10-year event EI30 maps for mainland China (available at https://doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001; Yue et al., 2020b). One-minute rainfall data from 62 stations, of which 18 had a record length > 29 years, were used to compute the “true” rainfall erosivity against which the new R-factor and 1-in-10-year EI30 maps were assessed to quantify the improvement over the existing maps through cross-validation. The results showed that (1) existing maps underestimated erosivity for most of the south-eastern part of China and overestimated for most of the western region; (2) the new R-factor map generated in this study had a median absolute relative error of 16 % for the western region, compared to 162 % for the existing map, and 18 % for the rest of China. The new 1-in-10-year EI30 map had a median absolute relative error of 14 % for the central and eastern regions of China, compared to 21 % for the existing map (map accuracy was not evaluated for the western region where the 1 min data were limited); (3) the R-factor map was improved mainly for the western region, because of an increase in the number of stations from 87 to 150 and temporal resolution from daily to hourly; (4) the benefit of increased station density for erosivity mapping is limited once the station density reached about 1 station per 10 000 km2.

During the winters (December–February) between 1985 and 2015, the North China Plain (NCP, 30–40.5∘ N, 112–121.5∘ E) suffered many periods of heavy haze, and these episodes were contemporaneous with extreme rainfall over southern China; i.e., south rainfall–north haze events. The formation of such haze events depends on meteorological conditions which are related to the atmospheric circulation associated with rainfall over southern China, but the underlying physical mechanism remains unclear. This study uses observations and model simulations to demonstrate that haze over the NCP is modulated by anomalous anticyclonic circulation caused by the two Rossby wave trains, in conjunction with the north–south circulation system, which ascends over southern China, moves north into northern China near 200–250 hPa, and then descends in the study area. Moreover, in response to rainfall heating, southern China is an obvious Rossby wave source, supporting waves along the subtropical westerly jet waveguide and finally strengthening anticyclonic circulation over the NCP. Composite analysis indicates that these changes lead to a stronger descending motion, higher relative humidity, and a weaker northerly wind, which favors the production and accumulation of haze over the NCP. A linear baroclinic model simulation reproduced the observed north–south circulation system reasonably well and supports the diagnostic analysis. Quasi-geostrophic vertical pressure velocity diagnostics were used to quantify the contributions to the north–south circulation system made by large-scale adiabatic forcing and diabatic heating (Q). The results indicated that the north–south circulation system is induced mainly by diabatic heating related to precipitation over southern China, and the effect of large-scale circulation is negligible. These results provide the basis for a more comprehensive understanding of the mechanisms that drive the formation of haze over the NCP.

Climate change is likely to worsen the food security situation through its impact on food production, which may indirectly affect fertility behaviour. This study examines the direct and indirect effects of climate change (e.g., temperature and precipitation) via the production of major crops, as well as their short- and long-term effects on the total fertility rate (TFR) in Bangladesh. We used structural equation modelling (SEM) to perform path analysis and distinguish the direct influence of climate change on fertility and its indirect influence on fertility through food security. We also applied the error correction model (ECM) to analyze the time-series data on temperature and precipitation, crop production and fertility rate of Bangladesh from 1966 to 2015. The results show that maximum temperature has a direct effect and indirect negative effect–via crop production–on TFR, while crop production has a direct positive effect and indirect negative effect–via infant mortality–on TFR. In the short term, TFR responds negatively to the maximum temperature but positively in the long term. The effect of rainfall on TFR is found to be direct, positive, but mainly short-term. Although indicators of economic development play an important part in the fertility decline in Bangladesh, some climate change parameters and crop production are non-negligible factors.

Photonic radar systems offer a promising solution for high-precision sensing in various applications, particularly in autonomous vehicles, where reliable detection of obstacles in real-time is critical for safety. However, environmental conditions such as atmospheric turbulence and rain attenuation significantly impact radar performance, potentially compromising detection accuracy. This study aims to assess the performance of a photonic radar system under different environmental scenarios, including free-space, Gamma-Gamma atmospheric turbulence, and light and heavy rain conditions, with a focus on detecting three distinct targets positioned at various distances. Our simulations demonstrate that Gamma-Gamma atmospheric turbulence introduces variability in the received signal, with fluctuations becoming more pronounced at greater distances. Additionally, rain attenuation was found to substantially degrade performance, with heavy rain causing up to a 1 dBm reduction in received power at 50 meters and nearly a 1.5 dBm reduction at 100 meters, compared to light rain. For three targets located at 50m, 100m, and 150m, the combined effects of rain and turbulence were particularly noticeable at longer distances, with the received power under heavy rain dropping to −100.4 dBm at 150 meters. These findings indicate the importance of accounting for environmental conditions in the design of photonic radar systems, especially for autonomous vehicle applications. Future improvements could focus on developing adaptive radar techniques to compensate for adverse weather effects, ensuring robust and reliable performance under varying operational conditions. The novelty of this study lies in the integration of photonic radar technology with an advanced modeling framework that accounts for both free-space propagation and adverse weather conditions. Unlike conventional radar studies, our work incorporates Gamma-Gamma turbulence modeling and rain attenuation effects to provide a more comprehensive analysis of radar performance in real-world environments. This study also proposes an optimized detection strategy for multiple targets at varying distances, demonstrating the potential of photonic radar for autonomous vehicle applications.

The primary factor influencing slope stability is the variation of internal mechanics within the soil-rock mixture caused by rainfall infiltration. Most existing research has focused on how rock content affects the failure of soil-rock mixture slopes. However, there has been insufficient investigation into the coupling effects of rainfall intensity and slope inclination on the stability of soil-rock mixture slopes. Therefore, the model test of soil-rock mixture slope was carried out. The coupling effects of rainfall intensity and slope inclination on water content, earth pressure, pore water pressure, and failure mode of soil-rock mixture slope were analyzed. The failure mode of soil-rock mixture slope induced by rainfall was revealed. The results indicated that an increase in rainfall intensity and slope inclination significantly contributed to the instability of soil-rock mixture slopes and the loss of fine particles. Additionally, the maximum values of water content, earth pressure, and pore water pressure increased progressively. Considering the two influencing factors of rainfall intensity and slope inclination, the calculation formulas related to the fine particle content, maximum water content, maximum earth pressure, and maximum pore water pressure of soil-rock mixture slope were established. The findings of this research provided theoretical support for the construction of soil-rock mixture slopes and the prevention and control of landslide disasters.

Sub6 GHz non‐line‐of‐sight signals are a potential opportunistic source of rainfall information that promises to improve the current urgent need regarding near‐surface rainfall detection, but the complex mechanisms in which these signals are impacted by rainfall have hindered further development in this area. In this study, we focus on four types of microwave propagation processes to explore the theoretical basis for Sub6 GHz signal sensitivity to rainfall. We also investigate how these signals change during rainy conditions using a cellphone signal recording experiment. The results demonstrate that the indirect effect of rainfall‐induced changes in the interfacial water film may significantly affect the Sub6 GHz signal, making it an opportunity to reflect rainfall information. Finally, we offer a comprehensive overview of the potential challenges, benefits, and drawbacks of low‐frequency non‐line‐of‐sight links in the context of rainfall inversion. Plain Language Summary This study investigates Sub6 GHz non‐line‐of‐sight signals, which are widely available and cost‐effective tools that have the potential to enhance our ability to detect rainfall patterns near the Earth's surface. This combination of theory and experiments gives us a better understanding about the behavior of low‐frequency non‐line‐of‐sight signals in rainy conditions. This knowledge can guide the development of innovative and improved methods for rainfall monitoring in the future through opportunistic means. Key Points The analysis provides insights into the physical mechanisms underlying this sensitivity of Sub6 GHz signals to rainfall The experimental results support the theoretical analysis and enhance the understanding of Sub6 GHz signal behavior in rainy environments This summary emphasizes the characteristics and challenges of low‐frequency non‐line‐of‐sight links in potential rainfall monitoring

Rainfall-runoff modeling is of great importance for flood forecast and water management. Hydrological modeling is the traditional and commonly used approach for rainfall-runoff modeling. In recent years, with the development of artificial intelligence technology, deep learning models, such as the long short-term memory (LSTM) model, are increasingly applied to rainfall-runoff modeling. However, current works do not consider the effect of rainfall spatial distribution information on the results. Focusing on 10 catchments from the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset, this study compared the performance of LSTM with different look-back windows (7, 15, 30, 180, 365 d) for future 1 d discharges and for future multi-day simulations (7, 15 d). Secondly, the differences between LSTMs as individual models trained independently in each catchment and LSTMs as regional models were also compared across 10 catchments. All models are driven by catchment mean rainfall data and spatially distributed rainfall data, respectively. The results demonstrate that regardless of whether LSTMs are trained independently in each catchment or trained as regional models, rainfall data with spatial information improves the performance of LSTMs compared to models driven by mean rainfall data. The LSTM as a regional model did not obtain better results than LSTM as an individual model in our study. However, we found that using spatially distributed rainfall data can reduce the difference between LSTM as a regional model and LSTM as an individual model. In summary, (a) adding information about the spatial distribution of the data is another way to improve the performance of LSTM where long-term rainfall records are absent, and (b) understanding and utilizing the spatial distribution information can help improve the performance of deep learning models in runoff simulations.

A novel high-permeability counterfort retaining wall (HPRW) was proposed for improved control of rainfall-induced landslides, and its working performance and mechanism were studied by thorough numerical simulations. The numerical simulations revealed that the retaining effect of the HPRW was significantly better than that of the conventional counterfort retaining wall (CRW) under the effect of rainfall. Relative to the CRW, the pore water pressure and groundwater table decreased owing to the excellent drainage capacity of the HPRW, in turn leading to the decreases in the hydrodynamic pressure and earth pressure. Consequently, the slope deformation decreased and stability of the slope increased with the application of the HPRW. Furthermore, the stress and displacement of the HPRW and the earth pressure acting on the HPRW were lower than those of the CRW under identical working conditions. Parametric analysis indicated that the rainfall intensity, property of the sliding mass and gravel filling in the catchment tank affected the retaining effect of the HPRW and the stability of the slope to varying degrees. The results of this study can provide a significant basis for the design, application and subsequent research on the HPRW.

Newly formed particles and their growth contribute significantly to cloud condensation nuclei. However, the effect of rain on new particle formation (NPF) is poorly understood. Rainfall removes the pre‐existing large particles by below‐cloud scavenging to reduce condensation and coagulation sinks by 4.0 × 10−2 and 1.5 × 10−4 s−1. The scavenging effect largely depends on the amount and duration of rain and the surface area concentration of the raindrops. NPF events can be facilitated by a combination of fewer condensation sinks and favorable meteorological conditions, contributing 3%–47% of PM2.5 (particulate matter smaller than 2.5 μm). The physical mechanism was confirmed by theoretical analysis. The results help elucidate the NPF process from a physical perspective, which can improve the prediction of the occurrence and duration of haze events. Until precursors are reduced, significant reductions in particulate matter (PM) may be difficult because NPF and its growth recharge the atmosphere with PM. Plain Language Summary New particle formation (NPF) significantly contributes to the ultrafine particle number and thus impacts the cloud properties. Newly formed particles may also be inhaled into the pulmonary alveoli and blood systems, harming human health. However, compared with the chemical mechanism, the underlying physical mechanism of NPF events is poorly understood, especially the relationship between rainfall and subsequent NPF. NPF events will be more frequent owing to the combination of warmer and wetter climates and fewer coagulation sinks in response to clean air action. This study deepens our understanding of the interaction between NPF and meteorology. A reduction in precursor emissions for NPF should be imposed to further reduce particulate matter. Key Points Positive correlation between N13‒25 and rainfall was showed in spring and summer New particle formation (NPF) was facilitated by reduced condensation sinks and favorable meteorology in response to rainfall Surface area concentration of the raindrops is a key parameter for modulating NPF