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This paper highlights detailed projected changes in rainfall over Thailand for the early (2011–2040), middle (2041–2070) and late (2071–2099) periods of the 21st century under the representative concentration pathways (RCP) 4.5 and RCP 8.5 using the high‐resolution multi‐model simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. The ensemble mean is calculated based on seven members consisting of six general circulation models (GCMs) and three regional climate models (RCMs). Generally, the ensemble mean precipitation agrees reasonably well with observations, best represented by the Global Precipitation Climatology Center (GPCC) data, over Thailand during the historical period (1976–2005). However, inter‐model variations can be large among ensemble members especially during dry months (December to March) for northern‐central‐eastern parts, and throughout the year for the southern parts of Thailand. Similarly for future projection periods, inter‐model variations in the sign and magnitude of changes exist. The ensemble means of projected changes in rainfall for both RCPs during dry months show distinct contrast between the northern‐central‐eastern parts and the southern parts of Thailand with generally wetter and drier conditions, respectively. The magnitude of change can be as high as 15% of the historical period, which varies depending on the sub‐region, season, projection period, and RCP scenario. In contrast, generally drier conditions are projected during the wet season (June to September) throughout the country for both RCPs where the rainfall reduction can be as high as 10% in some areas. However, the magnitude of projected rainfall changes of some individual models can be much larger than the ensemble means, exceeding 40% in some cases. These projected changes are related to the changes in regional circulations associated with the winter and summer monsoons, which are projected to weaken. The drier (wetter) condition is associated with the enhanced subsidence (rising motion). [ABSTRACT FROM AUTHOR]

The 1981–2010 monthly precipitation climatologies for Norway at 1 km resolution are presented. They are computed by an interpolation procedure (HCLIM+RK) combining the output from a numerical model with the in situ observations. Specifically, the regional climate model data set HCLIM‐AROME, based on the dynamical downscaling of the global ERA‐Interim reanalysis onto 2.5 km resolution, is considered together with 2009 rain‐gauges located within the model domain. The precipitation climatologies are defined by superimposing the grid of 1981–2010 monthly normals from the numerical model and the kriging interpolation of station residuals. The combined approach aims at improving the quality of gridded climatologies and at providing reliable precipitation gradients also over those remote Norwegian regions not covered by observations, especially over the northernmost mountainous areas. The integration of rain‐gauge data greatly reduces the original HCLIM‐AROME biases. The HCLIM+RK errors obtained from the leave‐one‐out station validation turn out to be lower than those provided by two considered interpolation schemes based on observations only: a multi‐linear local regression kriging (MLRK) and a local weighted linear regression (LWLR). As average over all months, the mean absolute (percentage) error is 10.0 mm (11%) for HCLIM+RK, and 11.4 (12%) and 11.6 mm (12%) for MLRK and LWLR, respectively. In addition, by comparing the results at both station and grid cell level, the accuracy of MLRK and LWLR is more sensitive to the spatial variability of station distribution over the domain and their interpolated fields are more affected by discontinuities and outliers, especially over those areas not covered by the rain‐gauge network. The obtained HCLIM+RK climatologies clearly depict the main west‐to‐east gradient occurring from the orographic precipitation regime of the coast to the more continental climate of the inland and it allows to point out the features of the climatic subzones of Norway. The paper presents the 1981–2010 monthly precipitation climatologies over Norway at 1‐km grid spacing. The climatologies are computed by an interpolation scheme (HCLIM+RK) combining the in situ observations with the regional climate model data set HCLIM‐AROME, based on the dynamical downscaling of the global ERA‐Interim reanalysis. The comparison with methods using observations only proved that HCLIM+RK improves the accuracy of Norwegian climatologies and provides reliable precipitation patterns also over the remote areas not covered by rain‐gauges. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper investigates winter (DJF) precipitation variability over East Anglia at the daily and 2 d timescales using rain gauge data from 134 sites. The first three principal components (PCs) explain 80.2% (82.0%) of the variance in daily (2 d totals). Precipitation displays higher spatial coherence in winter, with the first three PCs of summer daily totals (Neal and Phillips, 2009, International Journal of Climatology 29: 1661-1679) explaining only 72.3% of the variance. The winter PCs for both 24- and 48-h timescales display precipitation maxima in South Essex (PC 1), NE Norfolk (PC 2), and NW Cambridgeshire (PC 3). The rainfall maximum over South Essex is more important in winter than in summer. Lamb Weather Types and synoptic charts are used to explain the causes of the PCs. A precipitation regionalization is also produced. Discharge for 11 rivers is then modelled as a linear function of the rainfall patterns (as represented by the PC scores). With the exception of the River Babingley, whose discharge is not significantly related to the PCs, these models explain on average 26.5% of the variance. For most rivers, there are significant relationships between discharge and precipitation that fell up to 4 d ago on average. In general, East Anglian river flows show persistence over 6-8 d. [ABSTRACT FROM AUTHOR]

This paper examines the contribution of tropical--extratropical cloudbands, and of interactions between tropical cloud and mid-latitude systems, to cool season (April--October) rainfall in agriculturally marginal areas of Australia. A following paper describes inter- and intra-annual variability of these features. A classification scheme for these tropical influences based on GMS satellite imagery is described, and used to compile a 15-year archive of events. It is shown that cloudbands extending from the tropical oceans bordering Australia ('Oceanic' Cloudbands) are most frequent and influential between April and July, but decrease sharply after August, at which time bands originating over the continental interior ('Continental' Cloudbands) increase. The contribution of these systems to rainfall at stations representing agriculturally marginal areas is assessed. Oceanic Cloudbands originating west of 120 deg.E contribute 70--90 per cent of cool-season rain in north-western Australia, with the contribution decreasing to the south and east. North-eastern Australia receives a significant portion of its rain from Cloudbands originating east of 120 deg.E. Tropical--mid-latitude interactions are more important over eastern than western Australia, and produce some 30--40 per cent of rain over much of inland eastern Australia. The overall tropical influence (Cloudbands plus interactions) on rainfall is least in South Australia and western Victoria, but still amounts to some 35--40 per cent of cool-season rain in those areas. The proportion of events producing significant rainfall (>10 mm) is also examined: almost two- thirds of the Oceanic Cloudbands to affect western Australia produce significant rain, and about half of those affecting eastern Australia. [ABSTRACT FROM AUTHOR]

The pre-monsoon thermal field over the Indian landmass has an important bearing on the land-sea heating contrast in the region, consequently influencing the establishment, advance and overall performance ofthe Indian summer monsoon rainfall. This paper examines the relationship between the pre-monsoon thermal field over India and the following summer monsoon rainfall, in order to identify possible predictors for long-range forecasting of Indian summer monsoon rainfall. Based on the spatial patterns of correlations of monsoon rainfall with maximum and minimum temperatures at 121 stations well distributed over India, during the recent period 1951--80, two predictors showing a significant contribution to the variance in monsoon rainfall have been identified. They are (i) March minimum temperature in east peninsula India and (ii) May minimum temperature in west central India. These two predictors have performed very well in terms of their significant contribution to the multiple regression models during 1951--1987, vis-a-vis several other known predictors. They have also shown a consistently significant relationship with the monsoon rainfall during the recent period, from the mid-1940s till the end of the data period. A stepwise regression model for long-range forecasting of all-India summer monsoon rainfall, involving three regional predictors, has been developed, and has shown a multiple correlation of 0.89. ) 1997 by the Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

In his comment on my paper, Whetton argues that precipitation estimates for the Southern Ocean (high latitudes of the Southern Hemisphere) in the Legates and Willmott climatology are implausible owing to its large seasonality. Justification for this assertion includes comparisons with island observations and methodological limitations he feels exist in the Legates and Willmott climatology. In this reply, the methodology used by Legates and Willmott is examined carefully and Whetton's concerns about it are shown to be his misinterpretation of theintent of the air temperature correction. Our (Whetton's and my) arguments serve to illustrate that observational evidence of precipitation over the ocean expanse of the Southern Hemisphere is a major limiting factor of all observation-based climatologies. [ABSTRACT FROM AUTHOR]

The paper addresses issues relating to the design and calibration ofprecipitation models for down-scaling General Circulation Model (GCM) output. First, the significance of non-stationary daily circulation-pattern-precipitation relationships will be described for two UK sites, each with records beginning in 1881. Second, the cause(s) of the observed non-stationarity will be investigated using lengthy air flow, synoptic, temperature, and standardized teleconnection indices. Third, the prospects for incorporating these factors within down-scalingmethods will be discussed with reference to several alternative techniques. Finally, a multivariate approach incorporating teleconnectionand airflow indices within precipitation models is advocated. [ABSTRACT FROM AUTHOR]