Daily Precipitation Variability Research Articles

Variations and Trends in 115 Years of Graded Daily Precipitation Records at Three Hydrometeorological Stations in Finland

This study investigated the variability and trends in 115 years (1909–2023) of daily precipitation observed at three hydrometeorological stations in southern (Kaisaniemi), central (Kajaani), and northern (Sodankylä) Finland. We also identified the most significant climate teleconnections influencing daily precipitation variability at these three stations during the period 1951–2023. The daily precipitation records were primarily classified into six grades, including very light (≤1 mm), light (1–≤5 mm), moderate (5–≤10 mm), heavy (10–≤15 mm), very heavy (15–≤20 mm), and extreme (>20 mm). On average, the most intense daily precipitation was determined at the Kaisaniemi station in southern Finland. At this station, however, very light and light precipitation showed the lowest frequency, but other graded daily precipitation events were the most frequent. At all three stations, the intensity of very light precipitation significantly declined during the past 115 years, while its frequency increased. The highest rates of such decreases and increases in the intensity and frequency of very light daily precipitation were found at the Sodankylä stations in northern Finland, respectively, but the lowest rates were at the Kaisaniemi station in the south. At the Kajaani station in central Finland, the intensity of light precipitation decreased, but very heavy precipitation intensified. At this station, however, the number of both moderate and heavy precipitation events increased over time. Finally, historical variations in both the intensity and frequency of graded daily precipitation events in Finland showed significant relationships with different climate teleconnections, particularly the Scandinavia (SCAND) and the North Atlantic Oscillation (NAO) patterns.

Precipitation and Temperature Maxima: A Study across the Southern Great Plains Winter Wheat Region

Abstract With the importance of agriculture to the southern Great Plains (SGPs), accurate knowledge of growing season (GS) temperatures and precipitation is critical. Previous research into GS precipitation and temperature maxima leads to the development of the asynchronous difference index (ADI) which identified positive and negative ADI GSs (March–September). The goal of this research is to further investigate the ADI within a specific agricultural region of the SGP, the winter wheat region, and to quantify the temporal evolution of temperature and precipitation during positive and negative ADI GSs. For this, Global Historical Climatology Network (GHCN) daily station data were analyzed across the GS (March–September) from 1900 to 2020. Results show that differences appear in the temperature and precipitation fields when comparing positive and negative GSs. Namely, positive (negative) ADI GSs show positive (negative) precipitation and negative (positive) temperature anomalies early in the GS, with these anomalies flipping in the later portion of the GS. Further, the results of this work show that the depicted changes in temperature and precipitation during positive and negative ADI GSs impact winter wheat yields. Overall, these results analyze the implications of positive and negative ADI GSs on the SGP climate, namely, the impact of the seasonal variability of daily maximum temperature and precipitation on agriculture.

A multiscale approach to statistical downscaling of daily precipitation: Israel as a test case

AbstractRainfall in the Eastern Mediterranean is strongly modulated by complex topography and localized mesoscale processes. General circulation models (GCMs) struggle to capture daily precipitation variability in the region, both in time and in space. Rain in the Eastern Mediterranean occurs within a hierarchy of scales, as synoptic scale structures often drive local rainfall patterns. Daily rain prediction in the region can therefore benefit from analog downscaling—a nonlinear regression of a high‐resolution predictand from past synoptic‐scale predictors. We present a multiscaled downscaling algorithm of daily rain over Israel. The underlying goal is to create a mechanism‐based tool that will improve the analysis and prediction of precipitation on short time scales in models that cannot produce the field explicitly. We train the algorithm using coarse grid ERA5 reanalysis data and measurements from 21 rain gauges. The routine uses a k‐nearest neighbours algorithm to find the most similar past instances (i.e., analogs) for every predicted day. Analog selection is performed in two steps, based on scale (synoptic and local), as to not overshadow correlative but local predictors. The algorithm also includes several unique aspects tailored to Mediterranean climate: subdaily predictors of cyclone life cycles; representation of upper level cyclonic drivers; and the inclusion of rainfall potential using the Modified K‐Index (MKI). The proposed algorithm has better accuracy (66% correct predictions) compared to non‐downscaled reanalysis and climatological predictions. It better captures the spatial rainfall variance, mitigates the “drizzle bias,” and improves skill in extreme event prediction. However, it underestimates very rainy events and has trouble fully representing the spatial variance in the region. Nonetheless, our algorithm represents the potential for computationally inexpensive downscaling of daily precipitation in the Mediterranean with various possible applications, for example, characterization of droughts and storms, linking hydrological and synoptic scale processes and introducing uncertainty estimates using large ensembles.

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation

Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km.

Winter Orographic Precipitation and ENSO in Sapporo, Japan

The effect of global climate change on the distribution of snow water is a great concern. Thus, it is important to clarify the characteristics of winter precipitation variability, including mountain precipitation, together with climate indices. In this study, regional snowfall characteristics were investigated with the daily gridded precipitation over Sapporo City (located on the Japan Sea side of Hokkaido in northern Japan), which was quantified by the APHRODITE method and by adding local precipitation observation data. We found places of showing large interannual variability that is different from that of daily precipitation variability. Applying an EOF analysis to the daily grid precipitation, we defined four local precipitation types. The occurrence of each precipitation type and associated atmospheric circulation was analyzed, and the results revealed that (except for the Super El Niño winter of 1997/1998) more snow fell in the southwestern mountains and inland areas during El Niño winters, and more snow fell in the northeastern plains and along the sea during La Niña winters. Continued development and evaluation of the precise data that incorporate local precipitation network is needed.

Multiple comparisons of precipitation variations in different areas using simultaneous confidence intervals for all possible ratios of variances of several zero-inflated lognormal models.

Flash flooding and landslides regularly cause injury, death, and homelessness in Thailand. An advancedwarning system is necessary for predicting natural disasters, and analyzing the variability of daily precipitation might be usable in this regard. Moreover, analyzing the differences in precipitation data among multiple weather stations could be used to predict variations in meteorological conditions throughout the country. Since precipitation data in Thailand follow a zero-inflated lognormal (ZILN) distribution, multiple comparisons of precipitation variation in different areas can be addressed by using simultaneous confidence intervals (SCIs) for all possible pairwise ratios of variances of several ZILN models. Herein, we formulate SCIs using Bayesian, generalized pivotal quantity (GPQ), and parametric bootstrap (PB) approaches. The results of a simulation study provide insight into the performances of the SCIs. Those based on PB and the Bayesian approach via probability matching with the beta prior performed well in situations with a large amount of zero-inflated data with a large variance. Besides, the Bayesian based on the reference-beta prior and GPQ SCIs can be considered as alternative approaches for small-to-large and medium-to-large sample sizes from large population, respectively. These approaches were applied to estimate the precipitation variability among weather stations in lower southern Thailand to illustrate their efficacies.

Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature.An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 μg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.

Beyond the mean: long-term variabilities in precipitation and temperature on the Qinghai-Tibetan Plateau

AbstractLong-term variabilities in daily precipitation and temperature are critical for assessing the impacts of climate change on ecosystems. We characterized intra- and interannual variabilities in daily precipitation and temperature obtained from 1960 to 2015 at 78 meteorological stations on the Qinghai-Tibetan Plateau. The results show that 1) The intra-annual variability of daily precipitation increases for 55 meteorological stations with a rate of 0.08 mm per decade. In contrast, the intra-annual variability markedly decreases for daily mean, daytime mean, and nighttime mean temperatures with a rate of 0.09, 0.07, and 0.12 °C per decade, respectively at 90% or more of stations. 2) Variabilities of daily precipitation and temperatures are quite sensitive to high altitudes (> 3500 m). The intra- and interannual variabilities of daily precipitation significantly decrease at 1.0 and 0.07 mm per 1000 m, respectively. However, variations of high altitudes increase the intra- and interannual variabilities of daily mean temperature at 1.0 °C and 0.2 °C per 1000 m. Moreover, the interannual variability of nighttime mean temperature varies at 0.3 °C per 1000 m, the fastest rate among three temperature indices. 3) A larger mean annual precipitation is accompanied by a higher intra- and interannual variability of daily precipitation on the Qinghai-Tibetan Plateau; however, a higher mean annual temperature leads to lower variabilities of daily temperatures. This study illustrates that long-term climatic variability is understudied in alpine ecosystems characterized by high climatic sensitivity. Precipitation and temperature variabilities should be characterized to improve predictions of vulnerable ecosystems responding to climate change.

Large‐scale synoptic atmospheric moisture circulation patterns associated with variability of daily precipitation over East China

AbstractThis study identifies the variability of large‐scale atmospheric patterns (LSMPs) and atmospheric moisture circulation patterns associated with the variability of daily precipitation over East China using the self‐organizing maps (SOM) method. The spatiotemporal characteristics of the occurrence and intensity of daily precipitation were linked with the variabilities of LSMPs. Results show that the synoptic moisture transport over East China in both dry season and rainy season can be classified into eight patterns, and the occurrence frequency and primary occurring months of the eight patterns are varied. In the dry season, the wetter pattern (the drier pattern) exhibits a significant decreasing (increasing) trend with a slope of −0.23 (0.63)% year−1 at the confidence level of 0.95 during 1979–2018. The LSMPs corresponding to each atmospheric moisture circulation pattern, including the composite mean values of vertically integrated water vapour transport fields, 500 hPa geopotential height (Z500) anomalies and sea surface temperature anomalies (SSTA) in surrounding oceans, indicate that the primary oceanic moisture source of the dry season is western North Pacific (WNP), while that of the rainy season is WNP and the Indian Ocean. The location and intensity variabilities of subtropical high affect the transport and amount of water vapour along with its western flank to arrive in East China. SSTA has an influential link with the regional circulations and convection activity, and thus plays an important role in affecting water vapour transport.

Evaluation and Comparison of Daily GPM/TRMM Precipitation Products over the Tianshan Mountains in China

Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.

Cross-validating precipitation datasets in the Indus River basin

Abstract. Large uncertainty remains about the amount of precipitation falling in the Indus River basin, particularly in the more mountainous northern part. While rain gauge measurements are often considered as a reference, they provide information for specific, often sparse, locations (point observations) and are subject to underestimation, particularly in mountain areas. Satellite observations and reanalysis data can improve our knowledge but validating their results is often difficult. In this study, we offer a cross-validation of 20 gridded datasets based on rain gauge, satellite, and reanalysis data, including the most recent and less studied APHRODITE-2, MERRA2, and ERA5. This original approach to cross-validation alternatively uses each dataset as a reference and interprets the result according to their dependency on the reference. Most interestingly, we found that reanalyses represent the daily variability of precipitation as well as any observational datasets, particularly in winter. Therefore, we suggest that reanalyses offer better estimates than non-corrected rain-gauge-based datasets where underestimation is problematic. Specifically, ERA5 is the reanalysis that offers estimates of precipitation closest to observations, in terms of amounts, seasonality, and variability, from daily to multi-annual scale. By contrast, satellite observations bring limited improvement at the basin scale. For the rain-gauge-based datasets, APHRODITE has the finest temporal representation of the precipitation variability, yet it importantly underestimates the actual amount. GPCC products are the only datasets that include a correction factor of the rain gauge measurements, but this factor likely remains too small. These findings highlight the need for a systematic characterisation of the underestimation of rain gauge measurements.

Influence of Subseasonal Variability on the Diurnal Cycle of Precipitation on a Mountainous Island: The Case of New Caledonia

Abstract The relationship between the large-scale intraseasonal variability, synoptic wind regimes, and the local daily variability of precipitation over the main island of New Caledonia (southwest tropical Pacific) is investigated with a focus on the austral summer wet season (November–April). The average diurnal cycle of precipitation over the island is characterized by a sharp afternoon maximum around 1600 local time, with significant differences between the windward east coast, the leeward west coast, and the mountain range. The afternoon peak is related to the afternoon sea-breeze circulation and to the diurnal cycle of convection over land. In general, its magnitude follows the same evolution as the daily mean. In agreement with past studies, a clear modulation of the Madden–Julian oscillation (MJO) on both the diurnal cycle of precipitation and the probability of occurrence of four robust wind regimes can be identified in the New Caledonia region during the wet season. From the evidence that there is a qualitative correspondence between the effects of both the MJO phases and the wind regimes on features in the diurnal cycle of precipitation, a simple model is proposed to inspect the MJO forcing mediated by wind regimes on the diurnal variability of rain. The complete decomposition of the MJO impact shows that the modulation of diurnal cycle by the MJO relies on complex interactions between the MJO and synoptic winds that involve both large-scale MJO convective anomalies and MJO-induced modification of wind patterns.

Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

This study presents an assessment of the version-6 (V06) of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product from June 2014 to December 2017 over different hydro-climatic regimes in the Tianshan Mountains. The performance of IMERG-V06 was compared with IMERG-V05 and the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 precipitation products. The precipitation products were assessed against gauge-based daily and monthly precipitation observations over the entire spatial domain and five hydro-climatologically distinct sub-regions. Results showed that: (1) The spatiotemporal variability of average daily precipitation over the study domain was well represented by all products. (2) All products showed better correlations with the monthly gauge-based observations than the daily data. Compared to 3B42V7, both IMERG products presented a better agreement with gauge-based observations. (3) The estimation skills of all precipitation products showed significant spatial variations. Overall performance of all precipitation products was better in the Eastern region compared to the Middle and Western regions. (4) Satellite products were able to detect tiny precipitation events, but they were uncertain in capturing light and moderate precipitation events. (5) No significant improvements in the precipitation estimation skill of IMERG-V06 were found as compared to IMERG-V05. We deduce that the IMERG-V06 precipitation detection capability could not outperform the efficiency of IMERG-V05. This comparative evaluation of the research products of Global Precipitation Measurement (GPM) and TRMM products in the Tianshan Mountains is useful for data users and algorithm developers.

Impact of the spatial variability of daily precipitation on hydrological projections: A comparison of GCM‐ and RCM‐driven cases in the Han River basin, Korea

AbstractIn this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes.

Daily precipitation variability in the southern Alps since the late 19th century

We analysed a data set of 18 homogenized daily precipitation series from the southern European Alps, covering approximately the last 150 years. Previously available data from stations in Northern Italy have been extended considerably by recent digitization work, and, for the first time, they have been combined with daily data from Swiss stations on a centennial scale. Precipitation frequency in the southern Alps decreased significantly over the period 1890–2017. We show that this trend is related to a step‐like reduction in cyclonic weather types over central Europe that occurred around 1940. This decrease is an example of the large variability that affects precipitation in the region over many different time scales. In particular, strong trends on a decadal scale are related to the Atlantic Multidecadal Oscillation and the North Atlantic Oscillation, although the influence of the latter is present only in the recent decades. Trends in heavy precipitation indices do not show a coherent pattern across the study area. We find a significant increase in heavy precipitation in Switzerland, while a decrease affects the southeastern subset in spring.

Temperature and precipitation variability in regional climate models and driving global climate models: Total variance and its temporal‐scale components

This paper presents an evaluation of the variability and its temporal‐scale components of daily air temperature and precipitation simulated by two regional climate models (RCMs) and two driving global climate models (GCMs) from the EURO‐CORDEX and CMIP5 projects. The analysis was performed for eight geographical areas of central Europe for the period 1971–2000. After a brief evaluation of total variance, we focus on the ability of the simulations to represent short‐term, seasonal and long‐term variability components. This analysis was carried out for original time series as well as for series with the annual cycle removed. A fast Fourier transformation and a Kolmogorov–Zurbenko filter were used to separate the three temporal components.The two methods of time series decomposition generally agree, which confirms the robustness of the results. Although the observed total variability of daily air temperature and precipitation is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. Regarding the air temperature, the results for the time series with the annual cycle removed are generally somewhat worse than for original series. The simulations tend to underestimate the short‐term component of temperature variability, while the results for the other two components are inconclusive. Moreover, it was found that temperature variability caused by the annual cycle is overestimated by the simulation of HadGEM2 GCM and CCLM4‐8‐17 RCM driven by HadGEM2, which explains the relatively poor results of these simulations regarding air temperature total variance and short‐term as well as seasonal components in the original temperature series. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components.

Trends of daily precipitation concentration in Central-Southern Chile

The spatial and temporal variability of daily precipitation in Central-Southern Chile (~30–46°S), considering 176 meteorological stations for the period of 1965–2015, was analysed by mean of the concentration index (CI). The CI was calculated for the complete period, for the wet season and year-to-year, for each meteorological station. In addition, the trends were computed for the whole period by locations and by moving windows for the complete territory. The CI values range from 0.502 to 0.755, agreeing with previous works in the Mediterranean area and marine west coast climates. Linear correlations have been established between both year and wet season CI values with geographical factors, such as latitude, longitude and elevation. These correlations are statistically significant for the yearly CI with latitude and longitude, with −0.38 and − 0.39 Pearson's r values respectively (p-values lower than 0.001). However, the semiarid climates in Chile do not show the same behaviour as those presented in other regions of the world. The trends show an increase in the concentration of the whole series (mainly in the marine west coast climates) within the period between 1976 and 1994 with higher CI values, linked to the warm phase of the Pacific Decadal Oscillation.

Recent trends reveal decreasing intensity of daily precipitation in Spain

A comprehensive analysis of the spatial and temporal variability of daily precipitation in Spain in the period 1950–2012 was carried out based on the high‐resolution gridded data set SPREAD at a 5 × 5 km spatial resolution. Temporal trends of 12 daily precipitation indices were computed by application of the Sen's slope and the Mann–Kendall tests. The spatial distribution of the trends showed (a) a global slight increase in the duration of the precipitation events, especially along the Mediterranean fringe; (b) an increase of the frequency of low precipitation events and a decrease of the frequency of high and very high events (inverse in the Mediterranean coast); and (c) a slight decrease of the intensity when considering single events (1‐ or 5‐day duration), but a significant negative trend in mean and median precipitation when considering all the precipitation days (p > 0), especially in the Mediterranean coast.

Intraseasonal variability of winter precipitation over central asia and the western tibetan plateau from 1979 to 2013 and its relationship with the North Atlantic Oscillation

Winter precipitation over Central Asia and the western Tibetan Plateau (CAWTP) is mainly a result of the interaction between the westerly circulation and the high mountains around the plateau. Empirical Orthogonal Functions (EOFs), Singular Value Decomposition (SVD), linear regression and composite analysis were used to analyze winter daily precipitation and other meteorological elements in this region from 1979 to 2013, in order to understand how interactions between the regional circulation and topography affect the intraseasonal variability in precipitation. The SVD analysis shows that the winter daily precipitation variability distribution is characterized by a dipole pattern with opposite signs over the northern Pamir Plateau and over the Karakoram Himalaya, similar to the second mode of EOF analysis. This dipole pattern of precipitation anomaly is associated with local anomalies in both the 700hPa moisture transport and the 500hPa geopotential height and is probably caused by oscillations in the regional and large-scale circulations, which can influence the westerly disturbance tracks and water vapor transport. The linear regression shows that the anomalous mid-tropospheric circulation over CAWTP corresponds to an anti-phase variation of the 500hPa geopotential height anomalies over the southern and northern North Atlantic 10days earlier (at 95% significance level), that bears a similarity to the North Atlantic Oscillation (NAO). The composite analysis reveals that the NAO impacts the downstream regions including CAWTP by controlling south-north two branches of the middle latitude westerly circulation around the Eurasian border. During the positive phases of the NAO, the northern branch of the westerly circulation goes around the northwest Tibetan Plateau, whereas the southern branch encounters the southwest Tibetan Plateau, which leads to reduced precipitation over the northern Pamir Plateau and increased precipitation over the Karakoram Himalaya, and vice versa.