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Over the twentieth century, Sahel rainfall has undergone extreme variations on a decadal timescale. This study investigated the recent precipitation changes in West African Sahel using a high-resolution Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) product over the period 1981–2014. We found that the recent increase in precipitation results principally from an increase in the number of wet days (+10 d compared to the normal) over the entire West African Sahel band, along with an increase in the precipitation intensity over the central part of the West African Sahel (+3 mm d⁻¹). However, this overall increase in precipitation is associated with dry spells that are becoming more frequent but on average shorter over the entire West African Sahel band (on average by 30%), and with precipitation intensity that is decreasing (around 3 mm d⁻¹ during the study period) in the western part of the West African Sahel (Senegal). Such reorganization (i.e. weaker but more frequent precipitation) is expected to be beneficial for agriculture and society, reducing the likelihood of both flooding and droughts.

Although gridded precipitation products (GPPs) are essential when evaluating weather and climate systems, their reliability must be known before application for scientific works. This study assessed the correspondence of observed daily precipitation data over southwestern Iran and the corresponding precipitation values from two well-known GPPs, CHIRPS and CPC-Unified (CPC), from 1998 through 2018. The datasets were for the Jan-Mar period, which contributes to 30 to 80% of annual precipitation in northwestern Iran and Persian Gulf coastal areas. Principal component analysis was used to divide 218 rain-gauge stations in the study area into three main zones, the western and northwestern (Zone 1), south-central (zone 2), and coastal areas (zone 3). The area-averaged time series of zonal precipitation data were then compared with the corresponding zonal GPPs values. All precipitation data that was equal to or greater than 1 mm (wet day) and equaled or exceeded the 95th or 99th percentiles (very wet or extremely wet days, respectively) were compared for each zone. The comparison was repeated for 3-day cumulative precipitation data. The statistical tests demonstrated significant relationships between the observational-based and model-based GPPs. The results were more robust for the CPC and 3-day cumulative datasets than for CHIRPS and the daily records. The strongest results were for estimation of extremely wet days and were significant for CPC estimations of the three-day cumulative precipitation in zone 3.

The Northern Hemisphere winter-season wet day climatology is extremely important to hydrological and agricultural processes of the Himalayan region (HR). However, knowledge of expected changes in the winter-season wet day climatology under global warming is significantly limited. Hence, this study attempts to quantify the expected changes in winter-season wet day climatological patterns for HR during 2020–2099 in comparison to a baseline period of 1980–2000 under two different warming scenarios, these being representative concentration pathways 4.5 and 8.5 (RCP 4.5 and RCP 8.5). Five climate model products covering the southern Asian region were obtained from the Commonwealth Scientific and Industrial Research Organization initiated Coordinated Regional Climate Downscaling Experiment (CORDEX) of the World Climate Research Programme and used for this purpose. Model biases are estimated with respect to observations for a base line period of 1980–2000. Model ensemble non-linear trends of the winter-season wet days for the periods 2020–2040, 2041–2070, and 2071–2099 are estimated using Sen’s slope estimator, while ensemble average future changes in the number of winter-season wet days are estimated, and attempts made to identify the topographical ranges that are expected to be mostly affected by the changing winter-season wet day climatology. The results show that the CORDEX-regional climate models have a positive bias, ranging between 1 and 30 days, across the high altitudes of the entire Himalayas, and model performance improves with an increasing number of wet days per season. Although the impact of stronger warming (i.e. under RCP 8.5) is noted to enhance the area averaged non-linear trend of wet days over northwestern (0.014) and eastern (0.005) Himalaya during 2071–2099, the model ensemble predicted area-averaged reduction in the frequency of wet days of 0.3 to 1.0 day is highly likely by the end of this century. It is also observed that the Himalayan region within the range of 1000–2500 m above sea level may experience a decline in winter-season wet days by up to 0.8 to 3.2 days under the warming scenarios of both RCP 4.5 and 8.5.

Climate change presents recurring challenges in understanding extreme weather events, particularly the persistence of dry and wet periods. West Java is among the region's most vulnerable to such rainfall variability. This study analyses the relationship between consecutive dry days (CDD) and consecutive wet days (CWD). It estimates joint return periods (JRP) using a copula-based approach to assess the spatial characteristics of climate extremes in West Java. Marginal distributions were fitted for each indicator, followed by copula modelling using the Inference Function for Margins method and model selection based on the Akaike's information criterion (AIC). The inverse Gaussian (ING) distribution was most suitable for CDD, while the generalised extreme value (GEV) distribution best represented CWD. We found that the Gaussian and Frank copulas best captured the overall dependence structure between CDD and CWD. JRP analysis showed that simultaneous extremes (AND scheme) were significantly rarer than single-variable extremes (OR scheme). These findings provide valuable input for identifying high-risk areas and developing more locally adaptive climate risk mitigation strategies.

We test an equation for the probability of heavy 24 h precipitation amounts Pr(X > x) as a function of the wet-day frequency and the wet-day mean precipitation. The expression was evaluated against 9817 daily rain gauge records world-wide and was subsequently used to derive mathematical expressions for different rainfall statistics in terms of the wet-day frequency and the wet-day mean precipitation. This framework comprised expressions for probabilities, mean, variance, and return-values. We differentiated these statistics with respect to time and compared them to trends in number of rainy days and the mean rainfall intensity based on 1875 rain gauge records with more than 50 years of valid data over the period 1961-2018. The results indicate that there has been a general increase in the probability of precipitation exceeding 50 mm/day. The main cause for this increase has been a boost in the intensity of the rain, but there were also some cases where it has been due to more rainy days. In some limited regions there has also been an increase in Pr(X > 50 mm/day) that coincided with a decrease in the number of rainy days. We also found a general increasing trend in the variance and the 10-year return-value over 1961-2018 due to increasing wet-day frequency and wet-day mean precipitation.

Changes in soil moisture are likely to contribute to future changes in latent heat flux and various characteristics of daily precipitation. Such contributions during the second half of the twenty-first century are assessed using the simulations from the GLACE-CMIP5 experiment, applying a linear regression analysis to determine the magnitude of these contributions. As characteristics of daily precipitation, mean daily precipitation, the frequency of wet days and the intensity of precipitation on wet days are considered. Also, the frequency and length of extended wet and dry spells are studied. Particular focus is on the regional (for nine selected regions) as well as seasonal variations in the magnitude of the contributions of the projected differences in soil moisture to the future changes in latent heat flux and in the characteristics of daily precipitation. The results reveal the overall tendency that the projected differences in soil moisture contribute to the future changes in response to the anthropogenic climate forcing for all the meteorological variables considered here. These contributions are stronger and more robust (i.e., there are smaller deviations between individual climate models) for the latent heat flux than for the characteristics of daily precipitation. It is also found that the contributions of the differences in soil moisture to the future changes are generally stronger and more robust for the frequency of wet days than for the intensity of daily precipitation. Consistent with the contributions of the projected differences in soil moisture to the future changes in the frequency of wet days, soil moisture generally contributes to the future changes in the characteristics of wet and dry spells. The magnitude of these contributions does not differ systematically between the frequency and the length of such extended spells, but the contributions are generally slightly stronger for dry spells than for wet spells. Distinguishing between the nine selected regions and between the different seasons, it is found that the strength of the contributions of the differences in soil moisture to the future changes in the various meteorological variables varies by region and, in particular, by season. Similarly, the robustness of these contributions varies between the regions and in the course of the year. The importance of soil moisture changes for the future changes in various aspects of daily precipitation and other aspects of the hydrological cycle illustrates the need for a comprehensive and realistic representation of land surface processes and of land surface conditions in climate models.

The consequences of climate change are arising in the form of many types of natural disasters, such as flooding, drought, and tropical cyclones. Responding to climate change is a long horizontal run action that requires adaptation and mitigation strategies. Hence, future climate information is essential for developing effective strategies. This study explored the applicability of a statistical downscaling method, Bias-Corrected Spatial Disaggregation (BCSD), in downscaling climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and then applied the downscaled data to project the future condition of precipitation pattern and extreme events in Cambodia. We calculated four climate change indicators, namely mean precipitation changes, consecutive dry days (CDD), consecutive wet days (CWD), and maximum one-day precipitation (rx1day) under two shared socioeconomic pathways (SSPs) scenarios, which are SSP245 and SSP585. The results indicated the satisfactory performance of the BCSD method in capturing the spatial feature of orographic precipitation in Cambodia. The analysis of downscaled CMIP6 models shows that the mean precipitation in Cambodia increases during the wet season and slightly decreases in the dry season, and thus, there is a slight increase in annual rainfall. The projection of extreme climate indices shows that the CDD would likely increase under both climate change scenarios, indicating the potential threat of dry spells or drought events in Cambodia. In addition, CWD would likely increase under the SSP245 scenario and strongly decrease in the eastern part of the country under the SSP585 scenario, which inferred that the wet spell would have happened under the moderate scenario of climate change, but it would be the opposite under the SSP585 scenario. Moreover, rx1day would likely increase over most parts of Cambodia, especially under the SSP585 scenario at the end of the century. This can be inferred as a potential threat to extreme rainfall triggering flood events in the country due to climate change.

Climate science has confirmed the alteration of the hydrological cycle attributed to global warming. This warming tendency affects the monsoon precipitation in Pakistan with an unprecedented intensity, causing severe flooding. Therefore, it is inevitable to observe the recent spring and summer monsoon changes in extreme precipitation throughout Pakistan. The present study examined 8 precipitation indices in the past 50-year period (1971–2020) (stretched to two data periods; 1971–1998 and 1999–2020) using Mann–Kendall and Sen’s method to investigate the direction and magnitude of the observed trends. Spring and summer wet days significantly increased in the central eastern (Kakul, Kotli, Jhelum) and western (Cherat, Chitral, Peshawar) regions in the 1st data period but significantly decreased in areas including the southern region in the 2nd data period. We further observed the high-intensity precipitation days (R10, R20) in the same seasons. The intensity of summer R20 was much stronger throughout Pakistan in the 1st data period which reduced significantly during the 2nd data period in northern and southern regions. We extended the circle of investigation to very heavy and extreme precipitation (R30 and R50). The intensity of R30 and R50 in summer followed the same pattern as observed for R10 and R20. However, R30 and R50 in pre-monsoon significantly increased in the northern, east-western, and south-eastern regions during the 2nd data period. Summer monsoon and westerly humid regions experienced a decreasing tendency of very heavy and severe precipitation in the 1st data period. Our results concluded that the most significant changes in precipitation extremes occurred with higher intensity and recurring frequency for all indices in spring and summer monsoon during the 2nd data period.

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.

We analyze the relationship between total precipitation change and change in occurrence and intensity of wet days across West Africa using simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). While both changes in the intensity and occurrence of wet days contribute to the projected decrease of total precipitation over the West Sahel, there is a larger contribution from changes in the number of wet days, associated with a shorter precipitation season. The projected increase of total precipitation over the Central‐Eastern Sahel is connected primarily with changes in the intensity of wet days. Over the Guinean Coast, models disagree in how total precipitation will change, since they tend to show a decrease in the number of wet days combined with an increase in intensity of wet days. Evaluation of the models during the historical period shows they do not reproduce several key features of the observed relationships.