This study was based on daily rainfall data of 48 stations distributed over the entire island covering a 30-year period from 1981 to 2010. Data analysis was done to identify the spatial pattern of rainfall trends. The methods employed in data analysis are linear regression and interpolation by Universal Kriging and Radial Basis function. The slope of linear regression curves of 48 stations was used in interpolation. The regression coefficients show spatially and seasonally variable positive and negative trends of annual and seasonal rainfall. About half of the mean annual pentad series show negative trends, while the rest shows positive trends. By contrast, the rainfall trends of the Southwest Monsoon (SWM) season are predominantly negative throughout the country. The first phase of the Northeast Monsoon (NEM1) displays downward trends everywhere, with the exception of the Southeastern coastal area. The strongest negative trends were found in the Northeast and in the Central Highlands. The second phase (NEM2) is mostly positive, except in the Northeast. The Inter-Monsoon (IM) periods have predominantly upward trends almost everywhere, but still the trends in some parts of the Highlands and Northeast are negative. The long-term data at Watawala Nuwara Eliya and Sandringham show a consistent decline in the rainfall over the last 100 years, particularly during the SWM. There seems to be a faster decline in the rainfall in the last 3 decades. These trends are consistent with the observations in India. It is generally accepted that there has been changes in the circulation pattern. Weakening of the SWM circulation parameters caused by global warming appears to be the main causes of recent changes. Effect of the Asian Brown Cloud may also play a role in these changes.

This analysis is based on monthly means of rainfall at a dense network of gauging stations in Sri Lanka. The mean monthly values of rainfall at 646 stations were used as variables to characterise the individual stations. These variables show a significant correlation among most of them. The highest correlations were found between months within the same meteorological season, with one exception. The exception is that of October which has a higher correlation with months of southwest monsoon (SWM) than with the inter-monsoon (IM) months. The IM months and November have moderate values of correlation with the months of SWM. All three months of northeast monsoon (NEM) are strongly correlated and form a clearly defined group. This pattern of correlation can be explained in terms of the spatial distribution of rainfall of the 12 months. The strongly correlated months have a similar spatial pattern. This indicates that the number of distinct spatial modes of rainfall is less than 12. To discover these modes, principal component analysis (PCA) and factor analysis (FA) were applied on the data set. Of the two ordination methods, FA produced more easily interpretable results than PCA. The factor solution identified four spatiotemporal rainfall modes -weak southwest (SW) mode (March-April), strong SW mode (May-October), strong NEM mode (December-February) and mixed mode (November). These modes have strong similarity to the monthly rainfall surfaces created using the original data of the same periods.

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