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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.

In mountain terrain, well-configured high-resolution atmospheric models are able to simulate total annual rain and snowfall better than spatial estimates derived from in situ observational networks of precipitation gauges, and significantly better than radar or satellite-derived estimates. This conclusion is primarily based on comparisons with streamflow and snow in basins across the western United States and in Iceland, Europe, and Asia. Even though they outperform gridded datasets based on gauge networks, atmospheric models still disagree with each other on annual average precipitation and often disagree more on their representation of individual storms. Research to address these difficulties must make use of a wide range of observations (snow, streamflow, ecology, radar, satellite) and bring together scientists from different disciplines and a wide range of communities.

Much of the Eastern Cape province in South Africa has been experiencing a severe drought since 2015. This drought has had major socio-economic effects particularly on the large impoverished rural population as well as on some urban areas where supplied water services have broken down in several cases. The region is influenced by both midlatitude and tropical systems leading to a complex regional meteorology that hitherto has not been much studied compared to other parts of South Africa. Here, the ongoing drought is examined in the context of long-term trends and the interannual rainfall variability of the region. Although the region has experienced drought in all seasons since 2015, focus here is placed on the spring (September–November) which shows the most consistent and robust signal. On average, this season contributes between about 25–35% of the annual rainfall total. Based on CHIRPS data, it is found that this season shows a significant decreasing trend in both rainfall totals as well as the number of rainfall days (but not heavy rainfall days) for spring over most of the province since 1981. On interannual time scales, the results indicate that dry (wet) springs over the Eastern Cape are associated with a cyclonic (anticyclonic) anomaly southeast of South Africa as part of a shift in the zonal wavenumber 3 pattern in the midlatitudes. Over the landmass, a stronger (weaker) Botswana High is also apparent with increased (decreased) subsidence over and near the Eastern Cape which is less (more) favourable for cloud band development and hence reduced (enhanced) rainfall during dry (wet) springs. Analysis of mid-century (2040–2060) CMIP5 rainfall projections suggests that there may be a flattening of the annual cycle over the Eastern Cape with the winter becoming wetter and the summer drier. For the spring season of interest here, the multi-model projections also indicate drying but less pronounced than that projected for the summer.

Ethiopia’s agriculture is mostly dependent on rain, though the rainfall distribution and amount are varied in spatiotemporal context. The study was conducted to analyze the distribution, trends, and variability of monthly, seasonal, and annual rainfall data over the Wollo area from 1981 to 2022. To accomplish this, the study utilized the Climate Hazards Group Infrared Precipitation with Stations version two (CHIRPS-v2) data. Standard Rainfall Anomaly Index (SRA) and Coefficient of Variation (CV) were employed to examine rainfall variability and develop drought indices over southern Ethiopia. The Modified Mann Kendall (MMK) test, Sen’s slope estimator and the innovative trend analysis (ITA) were employed to detect temporal changes in rainfall trends over the study period. The study found that the area experienced considerable rainfall variability and change, resulting in extended drought and flood events within the study period. Results from SRA and CV revealed interannual and seasonal rainfall variability, with the proportions of years below and above the long-term mean being estimated at 56% and 44%, respectively. The MMK test showed that the annual rainfall during the Kiremt (summer-main rainy season) had an increasing trend. On the other hand, rainfall for the Belg (short rain season for the study area) season and the Bega (winter) season showed a significantly decreasing trend (p < 0.05). Results from the innovative trend analysis (ITA) also revealed that the annual and seasonal rainfall trends exhibited different trends in varied magnitude for different stations. On a spatial basis, the eastern and northeastern regions of the study area showed trends of increasing rainfall during the Kiremt (JJA). Decision-makers and development planners need to design strategies to mitigate the risks posed by changes in rainfall variability and distribution and enhance community adaptation and mitigation capacities in Wollo, Ethiopia.

The local weather and climate of the Himalayas are sensitive and interlinked with global-scale changes in climate, as the hydrology of this region is mainly governed by snow and glaciers. There are clear and strong indicators of climate change reported for the Himalayas, particularly the Jammu and Kashmir region situated in the western Himalayas. In this study, using observational data, detailed characteristics of long- and short-term as well as localized variations in temperature and precipitation are analyzed for these six meteorological stations, namely, Gulmarg, Pahalgam, Kokarnag, Qazigund, Kupwara and Srinagar during 1980–2016. All of these stations are located in Jammu and Kashmir, India. In addition to analysis of stations observations, we also utilized the dynamical downscaled simulations of WRF model and ERA-Interim (ERA-I) data for the study period. The annual and seasonal temperature and precipitation changes were analyzed by carrying out Mann–Kendall, linear regression, cumulative deviation and Student's t statistical tests. The results show an increase of 0.8 ∘C in average annual temperature over 37 years (from 1980 to 2016) with higher increase in maximum temperature (0.97 ∘C) compared to minimum temperature (0.76 ∘C). Analyses of annual mean temperature at all the stations reveal that the high-altitude stations of Pahalgam (1.13 ∘C) and Gulmarg (1.04 ∘C) exhibit a steep increase and statistically significant trends. The overall precipitation and temperature patterns in the valley show significant decreases and increases in the annual rainfall and temperature respectively. Seasonal analyses show significant increasing trends in the winter and spring temperatures at all stations, with prominent decreases in spring precipitation. In the present study, the observed long-term trends in temperature (∘Cyear-1) and precipitation (mm year−1) along with their respective standard errors during 1980–2016 are as follows: (i) 0.05 (0.01) and −16.7 (6.3) for Gulmarg, (ii) 0.04 (0.01) and −6.6 (2.9) for Srinagar, (iii) 0.04 (0.01) and −0.69 (4.79) for Kokarnag, (iv) 0.04 (0.01) and −0.13 (3.95) for Pahalgam, (v) 0.034 (0.01) and −5.5 (3.6) for Kupwara, and (vi) 0.01 (0.01) and −7.96 (4.5) for Qazigund. The present study also reveals that variation in temperature and precipitation during winter (December–March) has a close association with the North Atlantic Oscillation (NAO). Further, the observed temperature data (monthly averaged data for 1980–2016) at all the stations show a good correlation of 0.86 with the results of WRF and therefore the model downscaled simulations are considered a valid scientific tool for the studies of climate change in this region. Though the correlation between WRF model and observed precipitation is significantly strong, the WRF model significantly underestimates the rainfall amount, which necessitates the need for the sensitivity study of the model using the various microphysical parameterization schemes. The potential vorticities in the upper troposphere are obtained from ERA-I over the Jammu and Kashmir region and indicate that the extreme weather event of September 2014 occurred due to breaking of intense atmospheric Rossby wave activity over Kashmir. As the wave could transport a large amount of water vapor from both the Bay of Bengal and Arabian Sea and dump them over the Kashmir region through wave breaking, it probably resulted in the historical devastating flooding of the whole Kashmir valley in the first week of September 2014. This was accompanied by extreme rainfall events measuring more than 620 mm in some parts of the Pir Panjal range in the south Kashmir.

The southwest region of South Africa is the only part of southern Africa that predominantly receives its total annual rainfall during the austral winter months (April–September). In 2015–2017, this part of the country experienced extreme dry conditions which led to the severe water shortages experienced in the city of Cape Town. In this study, focused is placed on understanding the contribution of the early winter period (April–May) to wet and dry years in the southwestern part of South Africa. This period is of particular interest given its key role in the recent drought, the lack of previous work on this season, and climate change projections that the winter rainy season may shorten in duration. The early winter is found to be prone to dry conditions in recent decades, such that five of the six driest April–May in recent record have occurred after the year 2000. The dry early winters in particular tend to be associated with a weaker subtropical jet, less moisture flowing into the domain and a more stable atmosphere. It is found that although there is a moderate relationship between the Southern Annular Mode and early winter rainfall, it is not as strong as that compared to the full winter period. An analysis of CMIP5 models find that the projections portray the winter rainfall region in South Africa as being exposed to an increased likelihood of early winter dry conditions into the future (2040–2060). However, it remains a challenge for these models to reasonably capture the onset of winter rainfall in South Africa.

This study investigates the spatial and temporal patterns of trends and magnitude of rainfall on monthly, seasonal and annual time scales of 13 districts of Uttarakhand State located in Central Himalayan region of India. The temporal trend was analyzed using Mann-Kendall (MK), Modified Mann-Kendall (MMK), and Kendall Rank Correlation (KRC) tests at 10%, 5%, and 1% significance levels. The magnitude (slope) of rainfall trend (mm/year) was determined using Theil-Sen’s Slope (TSS) and Simple Linear Regression (SLR) tests. The autocorrelation coefficient (ACC) of three different time series was calculated at one-time lag and were tested at 10%, 5%, and 1% levels of significance for the application of MMK test. The results of analysis revealed significant positive and negative trends were observed in monthly, seasonal, and annual rainfall time series in all 13 districts of Uttarakhand state. The spatial variation of the trends based on monthly, seasonal, and annual rainfall time series data was interpolated using the Thiessen polygon (TP) method in ArcGIS 10.2 environment. The maps of spatial variability of rainfall trends were developed to help local stakeholders and water resource managers to understand the risk and vulnerability related to climate change in the region.

Simulations of daily rainfall for the region of Bologna produced by 13 climate models for the period 1850–2100 are compared with the historical series of daily rainfall observed in Bologna for the period 1850–2014 and analysed to assess meteorological drought changes up to 2100. In particular, we focus on monthly and annual rainfall data, seasonality, and drought events to derive information on the future development of critical events for water resource availability. The results show that historical data analysis under the assumption of stationarity provides more precautionary predictions for long-term meteorological droughts with respect to climate model simulations, thereby outlining that information integration is key to obtaining technical indications.

Drought monitoring and early warning (M & EW) systems are a crucial component of drought preparedness. M & EW systems typically make use of drought indicators such as the Standardised Precipitation Index (SPI), but such indicators are not widely used in the UK. More generally, such tools have not been well developed for hydrological (i.e. streamflow) drought. To fill these research gaps, this paper characterises meteorological and hydrological droughts, and the propagation from one to the other, using the SPI and the related Standardised Streamflow Index (SSI), with the objective of improving understanding of the drought hazard in the UK. SPI and SSI time series were calculated for 121 near-natural catchments in the UK for accumulation periods of 1–24 months. From these time series, drought events were identified and for each event, the duration and severity were calculated. The relationship between meteorological and hydrological drought was examined by cross-correlating the 1-month SSI with various SPI accumulation periods. Finally, the influence of climate and catchment properties on the hydrological drought characteristics and propagation was investigated. Results showed that at short accumulation periods meteorological drought characteristics showed little spatial variability, whilst hydrological drought characteristics showed fewer but longer and more severe droughts in the south and east than in the north and west of the UK. Propagation characteristics showed a similar spatial pattern with catchments underlain by productive aquifers, mostly in the south and east, having longer SPI accumulation periods strongly correlated with the 1-month SSI. For catchments in the north and west of the UK, which typically have little catchment storage, standard-period average annual rainfall was strongly correlated with hydrological drought and propagation characteristics. However, in the south and east, catchment properties describing storage (such as base flow index, the percentage of highly productive fractured rock and typical soil wetness) were more influential on hydrological drought characteristics. This knowledge forms a basis for more informed application of standardised indicators in the UK in the future, which could aid in the development of improved M & EW systems. Given the lack of studies applying standardised indicators to hydrological droughts, and the diversity of catchment types encompassed here, the findings could prove valuable for enhancing the hydrological aspects of drought M & EW systems in both the UK and elsewhere.

This study investigated the trends and variability of seasonal and annual rainfall and temperature data over southern Ethiopia using time series analysis for the period 1983–2016. Standard Anomaly Index (SAI), Coefficient of Variation (CV), Precipitations Concentration Index (PCI), and Standard Precipitation Index (SPI) were used to examine rainfall variability and develop drought indices over southern Ethiopia. Temporal changes of rainfall trends over the study period were detected using Mann Kendall (MK) trend test and Sen’s slope estimator. The results showed that the region experienced considerable rainfall variability and change that resulted in extended periods of drought and flood events within the study period. Results from SAI and SPI indicated an inter-annual rainfall variability with the proportions of years with below and above normal rainfall being estimated at 56% and 44% respectively. Results from the Mann Kendall trend test indicated an increasing trend of annual rainfall, Kiremt (summer) and Bega (dry) seasons whereas the Belg (spring) season rainfall showed a significant decreasing trend (p < 0.05). The annual rate of change for mean, maximum and minimum temperatures was found to be 0.042 °C, 0.027 °C, and 0.056 °C respectively. The findings from this study can be used by decision-makers in taking appropriate measures and interventions to avert the risks posed by changes in rainfall and temperature variability including extremes in order to enhance community adaptation and mitigation strategies in southern Ethiopia.